JP2016115287A - Test case generation program, test case generation method and test case generator - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a technique for generating a test case which satisfies coverage regulations in test comprehensiveness of MC/DC (modified condition/decision coverage) with enhanced satisfiability while eliminating redundancy.SOLUTION: The test case generation program causes a computer to execute an analysis steps of identifying a calculation object formula in which a truth value of a calculation result made by a calculation element and a truth value of a calculation object formula as an object of calculation by the calculation element satisfy a predetermined relationship, when plural calculation object formulae in the predetermined relationship are identified, to hold identified plural calculation object formulae, and to acquire a truth value of the calculation object formulae; and a selecting step of determining the satisfiability of the calculation object formula with respect to the plural calculation object formulae in a predetermined relationship to select a calculation object formula which is determined as certifiable.SELECTED DRAWING: Figure 9B

Description

  The present invention relates to a test case generation program, a test case generation method, and a test case generation apparatus.

MC / DC (Modified) is one of the indicators that define software testing quality standards.
Test coverage by Condition / Decision Coverage) is known. MC / DC is a kind of code coverage indicating the degree of coverage of tests executed on program code (internal logic) of software to be tested, and is used in the aircraft industry in the United States as coverage used when testing aircraft software. Developed by an organization. As another code coverage, for example, C / DC (Condition / Decision Coverage) that can confirm all the condition determinations in the program code of the software to be tested and all the branches as the determination results can be exemplified. Further, there can be exemplified MCC (Multiple Condition Coverage) or the like in which all combinations of the condition values included in the determination of the program code of the software to be tested can be confirmed with 2 n test cases (where n is the number of conditions).

MC / DC, for example, makes it possible to detect errors in “and” and “or” in judgment formulas that cannot be detected in C / DC, and program code in n + 1 or more test cases for n condition numbers. This is code coverage capable of confirming all combinations of condition values included in the determination. In this specification, expressions that obtain “true” and “false” are referred to as conditions, and expressions to be determined in a determination sentence of a program are referred to as determinations or determination expressions. The judgment formula can combine a plurality of conditions by logical operators such as “and” and “or”. The test coverage by MC / DC satisfies the following coverage rules.
• Coverage Rule 1: Test all entrances / exits of the program at least once.
• Coverage rule 2: All conditions included in the judgment of the program must be tested at least once for possible values.
-Coverage rule 3: All judgments of the program should test the possible values at least once.
• Coverage rule 4: Show that all conditions for judging a program affect the output of the judgment independently.

  In addition, the following patent documents exist as prior art documents in which technologies related to the technologies described in this specification are described.

JP 2011-198034 A JP 2013-143067 A JP 2009-181292 A

MC / DC test cases satisfying the test coverage of Coverage Rules 1-4 can be obtained according to the following procedures (1) to (4).
(1) Generate all combinations of true / false values for each condition in each judgment formula in the program to be tested.
(2) The true / false value of the determination formula corresponding to each true / false value combination is obtained.
(3) For each condition, the other condition is fixed, and when the truth value of one condition is changed, a set of test cases is obtained that also changes the truth value of the determination formula.
(4) A union set (which is the smallest) of each set of conditions becomes a test case that satisfies the coverage rule of the MC / DC test coverage.

  However, when generating a test case according to the procedure of (1)-(4), when generating all combinations of true / false values of each condition in (1), 2 n combinations (n is Condition number). For this reason, when the condition number n increases, there is a risk of causing a combination explosion for a combination of truth values.

  In the procedure (3), depending on the combination of the true / false values of the conditions, there is a possibility that a contradiction occurs between the conditions reflecting the true / false values. When a contradiction is included, it is difficult to obtain a test case that satisfies the test coverage of MC / DC with a combination of conditions reflecting true / false values.

  In one aspect, the present invention provides a technique for generating a test case that satisfies a coverage rule for MC / DC test coverage that can improve redundancy and eliminate redundancy.

  The above technique can be exemplified by the configuration of the following test case generation program. In other words, the test case generation program identifies a calculation target expression that satisfies a predetermined relationship between a true / false value of a calculation result obtained by an operator and a true / false value of a calculation target expression calculated by the operator. When a plurality of calculation target expressions in relation are specified, an analysis step for obtaining a truth value of the calculation target expression by holding the plurality of specified calculation target expressions and a plurality of calculation target expressions in a predetermined relationship A selection step of determining the satisfiability of the calculation target expression and selecting the calculation target expression determined to be satisfiable is executed.

  According to the test case generation program described above, it is possible to provide a technique for generating a test case that satisfies the coverage rule of the MC / DC test coverage degree with improved satisfiability and capable of eliminating redundancy.

It is explanatory drawing about the production | generation of the test case which satisfy | fills the test coverage of MC / DC of a comparative example. It is explanatory drawing about the production | generation of the test case which satisfy | fills the test coverage of MC / DC of a comparative example. It is explanatory drawing explaining the truth value relationship about a logical product and a logical sum. It is explanatory drawing explaining 3 types of true / false value relations which satisfy | fill the test coverage of MC / DC about a logical product and a logical sum. It is explanatory drawing explaining application of a combination rule. It is explanatory drawing explaining the combination of the truth value about each conditional expression in case of contradiction. FIG. 10 is an explanatory diagram illustrating a process of extracting a combination of true / false values of each condition from a lower-level processing process according to the second embodiment. It is a figure which shows the hardware structural example of the information processing apparatus of this embodiment. It is a figure explaining the processing block structure of the information processing apparatus of this embodiment. It is explanatory drawing explaining a binary tree conversion process. It is explanatory drawing explaining the production | generation process of the conditional expression map about a tree structure, and a conditional expression map set. It is explanatory drawing explaining the production | generation process of the conditional expression map set reflecting the truth value about the processing process of a node. It is explanatory drawing explaining the production | generation process of the conditional expression map about a tree structure, and a conditional expression map set concerning the 2nd truth value rule application process. It is explanatory drawing explaining the production | generation process of the conditional expression map set reflecting the truth value about the processing process of a node. It is explanatory drawing explaining the production | generation process of the conditional expression map about a tree structure, and a conditional expression map set concerning the 3rd truth value rule application process. It is explanatory drawing explaining the production | generation process of the conditional expression map about a tree structure, and a conditional expression map set concerning the 4th truth value rule application process. It is explanatory drawing explaining the production | generation process of the conditional expression map set reflecting the truth value about the processing process of a node. It is a figure which shows the example of a test case conditional expression table. It is explanatory drawing explaining a selection analysis process. It is a figure which shows the example of a test case conditional expression table before and behind an update. It is explanatory drawing explaining a test case production | generation process. It is a flowchart which illustrates the whole process of a test case production | generation process. It is a flowchart which illustrates the whole process of a test case production | generation process. It is a flowchart which illustrates the truth value rule application process of S2 illustrated in FIG. 9A. 9C is a flowchart illustrating the AND operator processing in S23 illustrated in FIG. 9C. 9C is a flowchart illustrating the AND operator processing in S23 illustrated in FIG. 9C. 9C is a flowchart illustrating the OR operator processing in S24 illustrated in FIG. 9C. 9C is a flowchart illustrating the OR operator processing in S24 illustrated in FIG. 9C. It is a flowchart which illustrates the test case selection process of S7 illustrated in FIG. 9B.

  Hereinafter, an information processing apparatus that executes a test case generation program according to an embodiment will be described with reference to the drawings. The configuration of the following embodiment is an exemplification, and the information processing apparatus is not limited to the configuration of the embodiment.

Hereinafter, an information processing apparatus in which a test case generation program is executed will be described with reference to the drawings of FIGS.
<Comparative example>
FIGS. 1A and 1B exemplify a diagram illustrating generation of a test case that satisfies the MC / DC test coverage as a comparative example. FIG. 1A is an example of a test target program in which a plurality of processing processes are described.

In the program example shown in block P1 of FIG. 1A, in the first step, whether or not the condition for each of the variables x and y such as “if (x> 2) and (y == 5) then return” is satisfied is satisfied. Describes processing by logical product connected by a logical operator with “and”. In the second step, “if (x == 3) or (y> 2) then z = 2 * Z” is a logical operator with “or” indicating whether the condition for each of the variables x and y is satisfied. Describes a logical OR process for deriving a variable z.

Test cases satisfying the MC / DC test coverage are generated, for example, so as to satisfy the following coverage rules 1-4 for the program at each step in FIG. 1A.
• Coverage Rule 1: Test all entrances / exits of the program at least once.
• Coverage rule 2: All conditions included in the judgment of the program must be tested at least once for possible values.
-Coverage rule 3: All judgments of the program should test the possible values at least once.
• Coverage rule 4: Show that all conditions for judging a program affect the output of the judgment independently. (The value of the other condition is fixed, and the value of one condition is changed to change the output value of the true / false value of the judgment)

  In the program of the block P1 in FIG. 1A, as shown in the block P2, the “entrance of the program” of the coverage rule 1 indicates, for example, the first step, and the “exit of the program” of the coverage rule 1 is the first Step, refers to the second step. Further, “judgment (also referred to as a judgment formula)” in the coverage rule 2-4 includes, for example, “(x> 2) and (y == 5)” in the first step, “(x == 3) or (y> 2) ”,“ condition ”means“ (x> 2) ”,“ (y == 5) ”in the first step,“ (x == 3) in the second step "," (Y> 2) ".

Note that the test case satisfying the MC / DC test coverage for the program illustrated in FIG. 1A is, for example, “(x = 3, y = 3)” for determining the processing process of the first and second steps. , “
A combination of numerical values for variables x and y such as (x = 0, y = 3) "can be exemplified. In the following description, the program of the second step illustrated in block P1 of FIG. A test case satisfying the / DC test coverage will be described.

As shown in block P4, the determination formula of the second step is an OR operation to determine whether the condition “(x == 3)” for the variable x and the condition “(y> 2)” for the variable y are satisfied. It is “(x == 3) or (y> 2)” connected by the child “or”. Therefore, the combination (x, y) of the variable values for the variables x and y included in each condition is a test case that satisfies the coverage rule 1.

For example, the variable value that satisfies the condition “(x == 3)” for the variable x is “3”, and other variable values other than the variable value “3” have the condition “(x == 3)”. This is the variable value that is not satisfied. Further, the variable value that satisfies the condition “(y> 2)” for the variable y is a variable value in a range exceeding “2”, and the variable value in the range of the variable value “2” or less is the condition “(y> 2) Variable value that makes "" not true.

  Therefore, the test using the variable value “3” that satisfies the condition “(x == 3)” and the variable value that does not satisfy the condition “(x == 3)”, for example, “1” The possible values of the condition “(x == 3)” for the variable x included in the step judgment formula will be tested at least once. For this reason, the variable values “1” and “3” for the variable x are included in the test case that satisfies the coverage rule 2 of MC / DC.

Similarly for the variable y, a variable value that satisfies the condition “(y> 2)”, for example, “3”,
In addition, a test using a variable value that fails to satisfy the condition “(y> 2)”, for example, “1” is possible for the condition “(y> 2)” for the variable y included in the determination formula of the second step. The correct value will be tested at least once. For this reason, the variable values “1” and “3” for the variable y are included in the test case that satisfies the coverage rule 2 of MC / DC.

A test case (x = 1, y = 1) that is a combination of variable values for variables x and y with respect to the conditions “(x == 3)” and “(y> 2)” of the determination formula of the second step , (X = 1, y = 3), (x = 3, y = 1) are MC / D
C coverage rule 2 is satisfied.

A combination of variables x and y with (x = 3, y = 3) may also exist as a test case that satisfies the coverage rule 2 of MC / DC. However, the condition establishment test for the condition “(x == 3)” included in the determination formula of the second step is executed at least once by (x = 3, y = 1). The condition satisfaction test for the condition (y> 2) ”is executed at least once by (x = 1, y = 3). In MC / DC Coverage Rule 2, the possible variable values have to be tested at least once for the conditions “(x == 3)” and “(y> 2)”, so (x = 3, y = Since the combination with 3) overlaps with whether or not the condition is satisfied by another test case, it becomes an extra test case.

Test cases satisfying MC / DC Coverage Rule 2 (x = 1, y = 1), (x = 1, y = 3), (x = 3, y = 1
), The combination with (x = 1, y = 1) is a test case that does not hold the determination formula “(x == 3) or (y> 2)” in the second step. The combination with (x = 1, y = 3) is a test case that establishes the determination formula “(X == 3) or (y> 2)” in the second step. For this reason, for example, MC / D
In a test case that satisfies C Coverage Rule 2, the combination of (x = 1, y = 1) and (x = 1, y = 3) for variables x and y can be a judgment formula for the program in the second step. This is a test case for testing the correct value at least once. A test case with a combination of (x = 1, y = 1) and (x = 1, y = 3) for the variables x and y satisfies the coverage rule 3 of MC / DC.

  Here, regarding the condition “(x == 3)” included in the determination formula of the program in the second step, the value “3” of the variable x that satisfies the condition can be said to be a variable value of “true”. The value “1” that does not satisfy the condition can be said to be a “false” variable value. Similarly, for the condition “(y> 2)”, the value “3” of the variable y that satisfies the condition can be said to be a variable value of “true”, and the value “1” that does not satisfy the condition is “ It can be said that the variable value is “false”.

For example, the combination of (x = 1, y = 1) for the variables x and y is “false” for the condition “(x == 3)” and “false (for the condition“ (y> 2) ”. false) ”, which is a test case in which the judgment formula“ (x == 3) or (y> 2) ”of the program is not established (false). Further, for example, (x = 3, y = 1) for the variables x and y is “true” for the condition “(x == 3)” and “false (for the condition“ (y> 2) ”. false) ”, and this is a test case that satisfies (true) the determination formula“ (x == 3) or (y> 2) ”of the program.

The relationship between the test case (x = 1, y = 1) and (x = 3, y = 1) is as follows: the truth value for the condition “(y> 2)” is fixed to “false”. There is a relationship of changing the truth value for “(x == 3)”, and a relationship of changing the truth value of the judgment formula “(x == 3) or (y> 2)” of the program. In other words, the relationship between the test case (x = 1, y = 1) and (x = 3, y = 1) is determined by the determination formula “(x == 3) of the program in the second step.
or (y> 2) ”will affect the truth value output independently.

For example, the combination of (x = 1, y = 3) for the variables x and y is “false” for the condition “(x == 3)” and “false” for the condition “(y> 2)”. This is a test case where “true” is satisfied and the judgment formula “(x == 3) or (y> 2)” of the program is satisfied (true). Therefore, the relationship between the test case (x = 1, y = 1) and (x = 1, y = 3) is fixed to “false” for the condition “(x == 3)” In addition, there is a relationship in which the truth value for the condition “(y> 2)” is changed, and there is a relationship in which the truth value of the program judgment expression “(x == 3) or (y> 2)” is changed. The relationship between the test case (x = 1, y = 1) and (x = 1, y = 3) is true of the judgment formula “(x == 3) or (y> 2)” in the second step program. It will affect the output of false values independently.

For the second step program, a combination of test cases (x = 1, y = 1) and (x = 3, y = 1), and (x = 1, y = 1) and (x = 1, y = 3) is the union of test cases satisfying MC / DC coverage rule 4. Therefore, in the second step program in which the number of conditions included in the determination formula is 2, three test cases “(x = 1, y = 1), (x = 1, y = 3), (x = 3, y = 1) ". When the number of conditions related to the judgment formula of the program to be tested is n, n + 1 or more test cases satisfying the MC / DC test coverage are generated with n + 1 as a minimum.

Note that the test cases satisfying the MC / DC test coverage can test the true / false values of each condition related to the judgment formula of the program to be tested, and test the true / false values of the judgment formula of the program. Anything is possible. For example, for the second step program, combinations of variable values of variables x and y are “(x = 2, y = 2), (x = 2, y = 4), (x = 3, y = 2) ”
Such a test case may be used.

Next, a method (procedure) for obtaining an MC / DC test case satisfying the test coverage of the coverage rule 1-4 will be described with reference to FIG. 1B. FIG. 1B is an explanatory diagram illustrating a method (procedure) for obtaining an MC / DC test case that satisfies the test coverage of the coverage rule 1-4. However, the program as an explanatory example is “if (A or B) and C then z = 2 * Z”. In the following description, “(A or B) and C” is a determination formula, and “A”, “B”, and “C” are conditions for determination. Each condition relating to the determination has a true / false value, and the true / false value of the determination formula as a program is determined by a combination of the true / false values for each condition.

  A test case satisfying MC / DC test coverage with “if (A or B) and C then z = 2 * Z” as a test target is obtained according to the following procedures (1) to (4). be able to.

(1) Generate all combinations of true / false values for each condition.
As illustrated in a region Z1 surrounded by a broken line in FIG. 1B, all combinations of true / false values are generated for the determination conditions “A”, “B”, and “C”. In the example of FIG. 1B, since the number of conditions related to the determination is 3, eight combinations of true / false values (identification numbers 1-8), which are 2 to the cube, are generated.

(2) The true / false value of the determination formula corresponding to each true / false value combination is obtained.
As illustrated in the region Z2 surrounded by the broken line in FIG. 1B, the truth value of the judgment expression for all combinations of the truth values generated for the conditions “A”, “B”, and “C” relating to judgment. Is generated. Here, the judgment formula of the program is “(A or B) and C”.

The left term (A or B) of the judgment expression connected by logical product (and) is the logical sum of the conditions “A” and “B”, so if at least one of the conditions is “true” The left term (A or B) is “true”. Three combinations of (A, B) = (true, true), (true, false), (false, true) as a combination of the truth values of the conditions "A" and "B" are connected by a logical sum (or). The left term (A or B) is “true”.

And in the judgment formula (A or B) and C ”connected by logical product, the left term“ (A or B) ”and the right term“ C ”
"" Is "true", the true / false value as the judgment formula is "true", so the condition "A"
, “B”, “C” boolean combinations (A, B, C) = (true, true, true), (true, false, true),
(False, true, true) makes the judgment formula “(A or B) and C” “true” (in other combinations, the judgment formula is “false”). In the example of FIG. 1B, the combination of the conditions of identification numbers 1, 3, and 5 makes the determination expression “(A or B) and C” “true”.

(3) For each condition, the other condition is fixed, and when the truth value of one condition is changed, a set of test cases is obtained that also changes the truth value of the determination formula.
As illustrated in a region Z3 surrounded by a broken line in FIG. 1B, a truth value for satisfying the MC / DC coverage rule 4 for each of the conditions “A”, “B”, and “C” relating to determination. Find a combination of For example, in the case of the condition “A”, the truth values of the conditions “B” and “C” are fixed, and the truth value of the condition “A” is changed to “true” and “false”. In such a case, a combination in which the true / false value of the region Z2 is changed is obtained. Similarly, in the case of the condition “B”, the true / false values of the conditions “A” and “C” are fixed, and the true / false values of the condition “B” are set to “true” and “false”. When a change is made, a combination in which the true / false value of the region Z2 is changed is obtained. In the case of condition “C”, conditions “A” and “B”
When the truth value of the condition “C” is changed to “true” and “false”, a combination that changes the truth value of the region Z2 is obtained.

In FIG. 1B, for the condition “A”, when the conditions “B” and “C” of the identification numbers 1 and 5 are fixed to (true) and (true), the identification numbers 2 and 6 (true When fixed to (true) or false (false)), the truth value of the judgment expression does not change regardless of the change of the truth value of the condition “A”.
In addition, even in the case of identification numbers 4 and 8 in which the conditions “B” and “C” are fixed to (false) and false (false), regardless of the change of the truth value of the condition “A”, the judgment formula The truth value of does not change.

On the other hand, for the condition “A”, when the conditions “B” and “C” of the identification numbers 3 and 7 are fixed to (false, true), the truth value of the condition “A” is changed. As a result, the true / false value of the determination formula is changed. Therefore, for the conditions “A”, “B”, and “C”, the combination of the true / false values of identification number 3 (true, false, true) and identification number 7 (false ( A combination of false values of “false”, “false”, and “true” is a true / false relationship satisfying the coverage rule 4 of MC / DC.

Similarly for the condition “B”, the conditions “A” and “C” of the identification numbers 1 and 3 are set to “true”.
, True), or fixed to (true) or false (false) for identification numbers 2 and 6, regardless of changes in the true / false value of condition “B” The truth value of the expression does not change. In addition, even in the case of identification numbers 6 and 8 in which the conditions “A” and “C” are fixed to (false) and false (false), regardless of the change of the truth value of the condition “B”, the judgment formula The truth value of does not change.

On the other hand, for the condition “B”, when the conditions “A” and “C” of the identification numbers 5 and 7 are fixed to (false), true (true), the truth value of the condition “B” is changed. As a result, the true / false value of the determination formula is changed. Therefore, for the conditions “A”, “B”, and “C”, a combination of a false value of (false), true (true), and identification number 7 (false ( A combination of false values of “false”, “false”, and “true” is a true / false relationship satisfying the coverage rule 4 of MC / DC.

For the condition “C”, when the conditions “A” and “B” of the identification numbers 7 and 8 are fixed to (false, false), the truth value of the condition “C” is changed. Regardless, the truth value of the judgment formula does not change. When the other conditions “A” and “B” of the identification numbers 1 and 2 are fixed to (true) and “true”, the identification numbers 3 and 4 (true) and false (false) ) Is fixed, the true / false value of the determination formula is changed according to the change of the true / false value of the condition “C”. Even when the identification numbers 5 and 6 are fixed to (false, true), the truth value of the determination formula is changed according to the change of the truth value of the condition “C”.
Therefore, for the condition “C”, the combination of the truth values of the identification numbers 1 and 2, 3 and 4, 5 and 6 is
The true / false relationship satisfies the MC / DC coverage rule 4.

(4) A union set (which is the smallest) of each set of conditions becomes a test case that satisfies the coverage rule 1-4 of the test coverage of MC / DC.
In the example of FIG. 1B, the combination of true / false values for the condition “A” is one set of identification numbers 3 and 7, and the combination of true / false values for the condition “B” is one set of identification numbers 5 and 7. It is. Further, there are three combinations of truth values for the condition “C”: identification numbers 1, 2, 3, 4, 5, and 6. Finding the minimum union of test cases for the target program by having at least one set of true / false values as test cases for each condition from among the combinations of true / false values for each condition Can do.

Here, when one set of identification numbers 1 and 2 is selected from the three combinations of true / false values for the condition “C”, the true / false as a test case is combined with the conditions “A” and “B”. The total number of combinations of values is five, identification numbers 1, 2, 3, 5, and 7. When one set of identification numbers 3 and 4 is selected, the identification number 3 overlaps with the combination that becomes the test case of the condition “A”. There are four ways of 4, 5, and 7. Similarly, when one set of identification numbers 5 and 6 is selected, since the identification number 5 overlaps with the combination that becomes the test case of the condition “B”, the total number of combinations of the truth values that become the test cases is the identification number. There are four types of 3, 5, 6, and 7.

Therefore, for the combination of truth values for the conditions “A”, “B”, and “C”, the union that minimizes the total number of truth value combinations as test cases is the identification number 3, 4, 5, 7 Or a test case with identification numbers 3, 5, 6, and 7.

  In this way, the above-described procedures (1) to (4) are applied to the judgment formulas of the program to be tested and the conditions included in the judgment formulas, and n + 1 ways in which n + 1 is the minimum value for the condition number n. Test cases satisfying the above MC / DC test coverage can be generated. However, in the procedure (1), since all combinations of the true / false values of the conditions included in the determination formula are generated, 2 n combinations of true / false values are generated for the condition number n. When the number of conditions included in the program to be tested is relatively large, the combination of true / false values increases in proportion to the increase in the condition number n, which may cause a so-called combination explosion. When a combination explosion occurs, the test case generation process that satisfies the MC / DC test coverage may not converge.

<Example 1>
As described with reference to FIG. 1B, among the combinations of true / false values for each condition included in the judgment formula of the program to be tested (eight in FIG. 1B), the coverage rule for MC / DC test coverage is satisfied. A combination of truth values other than the minimum number of combinations (four patterns in FIG. 1B) is redundant.

  A test case that satisfies the test coverage of MC / DC is defined as “test all possible conditions included in the program judgment at least once for the judgment rule to be tested” for the judgment formula to be tested. It is required to satisfy the truth value relationship. Similarly, a test case satisfying the MC / DC test coverage level must be tested at least once with respect to the judgment formula to be tested, which is “a value that can be used for all judgments of the program”. It is required to satisfy a true / false relationship such as

Here, the truth value for the judgment formula of the program to be tested is determined based on logical operators such as “and” and “or” that combine the conditions. In the first embodiment, a true / false value as a determination expression and a combination of true / false relations for each condition are satisfied so as to satisfy the above-described coverage rules 2 and 3 based on the logical relationship of logical operators that combine the conditions. Thus, an excessive combination of true and false values for each condition is excluded.

(basic rule)
FIGS. 2A (1) and 2 (2) illustrate explanatory views of the true / false relationship regarding the logical product (and) and the logical sum (or) included in the determination formula to be tested. FIG. 2A (1) is an explanatory diagram of a true / false value relationship for logical product, and FIG. 2A (2) is an explanatory diagram of a true / false value relationship for logical sum. Note that the determination formula in the explanation example is “(A or B) and (C)”.

In the relationship of logical product: (A and B) in FIG. 2A (1), for example, (A, B) = (true), true ( (true)), (true), false (false)), (false), true (true)), (false (false), false (false)), there are four combinations. Then, for the four combinations of true / false values for each condition, the logical product judgment results are “true” and “false” so as to satisfy the coverage rule 3 of the MC / DC test coverage. A combination of the conditions “is extracted.

First, the condition for setting the logical product determination result to “true” is (A, B) = (true), true among the four combinations of true / false values of each condition. ). Other combinations of (A, B) = (true, false), (false), true (true), (false), false This is a combination of conditions in which the determination result is “false.” MC /
In order to satisfy the coverage rule 3 of DC test coverage, the logical product judgment result is “false”.
It is only necessary to extract at least one or more from three combinations of true / false values for each condition.

Next, for the combination of the truth values of the condition (A, B) that makes the logical product determination result “true”, the logical product is satisfied so that the coverage rule 2 of the test coverage of MC / DC is satisfied. A predetermined combination is extracted from three combinations of true / false values under each condition where the determination result is “false”.

  Here, for each of the conditions “A” and “B”, a combination of true / false values for which the logical product determination result is “true” has already been determined. Therefore, in order to satisfy the coverage rule 2 of the MC / DC test coverage, it is only necessary to have a combination of conditions “A” and “B” that can be tested with the condition “A” as “false”. Similarly, there may be a combination of conditions “A” and “B” that can be tested with the condition “B” set to “false”.

As a result, in the true / false relation of the conditions connected by AND, the condition “A” is set to “false” (A, B) = (false (true), true) combination, condition A combination of “A” and “B” = “false” (A, B) = (true, false) satisfies the relationship of the coverage rule 2. Of the four combinations of true / false values for each condition, (A, B) = (false (false), false (false
)) Is a surplus combination because the test of true / false values is covered by the other two combinations in which the logical product determination result is “false”.

In the true / false relation of the conditions connected by the logical product: (A and B), three combinations of true / false values for the conditions “A” and “B” illustrated in FIG. / DC can be presented as a combination that satisfies the coverage rules 2 and 3 of the test coverage. Logical product: In (A and B), the combination of the truth values of the conditions "A" and "B" is (A, B) = (true, true), (true), false (False)), (false), and true (true) are extracted as combinations that satisfy the test coverage of MC / DC.

A similar relationship can be extracted for the logical sum (A or B) in FIG. 2A (2). That is, for logical sum, the combination of the truth values of the conditions “A” and “B” is (A, B) = (true)
, True), (true), false (true), (false), true (true), (false), false (false)), there are four combinations . Then, for the four combinations of true / false values of each condition, the logical OR determination result is “true” and “false” so as to satisfy the coverage rule 3 of the MC / DC test coverage. A combination of the conditions “is extracted.

First, the condition that the logical OR determination result is “false” is (A, B) = (false) or false (false) among the four combinations of true / false values of each condition. ). Other combinations of (A, B) = (true, true), (true), false (false), (false, true) This is a combination of conditions that makes the determination result “true”. In order to satisfy the coverage rule 3 of the MC / DC test coverage, at least one or more types are extracted from combinations of three true / false values for each condition with the result of logical sum being “true”. do it.

Next, for the combination of the true / false values of the condition (A, B) with the determination result of the logical sum being “false”, the logical sum is satisfied so as to satisfy the coverage rule 2 of the test coverage of MC / DC. The judgment result of “true (tr
ue) ”, a predetermined combination is extracted from three combinations of true / false values of each condition.

Here, for each of the conditions “A” and “B”, the logical OR determination result is “false”.
The combination of true / false values is already fixed. Therefore, in order to satisfy the coverage rule 2 for the MC / DC test coverage, the conditions “A”, “B” that can be tested with the condition “A” as “true”.
Similarly, there may be a combination of conditions “A” and “B” that can be tested with the condition “B” as “true”.

As a result, in the true / false relationship of the conditions connected by the logical sum, the condition “A” is set to “true” (A, B) = (true, false), a combination of the conditions A combination of (A, B) = (false, true) where “B” is “true” satisfies the relationship of the coverage rule 2. Of the four combinations of truth values for each condition, (A, B) = (true), true (true)
)) Is a surplus combination because the true / false test is covered by the other two combinations in which the logical sum determination result is “true”.

In the true / false relationship of the conditions connected by the logical sum: (A or B), the combination of three true / false values for the conditions “A” and “B” illustrated in FIG. / DC can be presented as a combination that satisfies the coverage rules 2 and 3 of the test coverage. For logical OR: (A or B), the condition “A”
, (A, B) = (true, false), (false, true), (false), false (false) )) Are extracted as combinations satisfying the MC / DC test coverage.

  In this way, if the true / false values for each condition are applied hierarchically (recursively) in the order of operation in accordance with the logical type of the logical operator that combines the conditions of the judgment expression, n equal to 2 for the condition number n A combination of true / false values that satisfies the test coverage of MC / DC can be generated without generating a combination of true / false values.

For example, in the judgment expression “(A or B) and (C)”, the logical sum of the conditions “A” and “B” is a lower processing process, and the logical sum of the conditions “A” and “B” (A or B) and the condition (C) are the upper processing processes.

  For example, for the condition (A or B) and the condition (C) connected by the logical operator “and”, ((A or B), (C)) = ( Three combinations of “true”, “true”, “true”, “false”, and “false” are generated. Then, for example, the true / false relationship of the logical sum regarding the condition (A or B) connected by the logical operator “or” may be applied to each combination of the generated true / false values. In other words, for the condition (A or B), the condition “A” that makes the logical sum “true”, the combination of the truth values of “B”, the condition “A” that makes the logical sum “false” The combination of true / false values of “B” is applied to the three combinations of true / false values obtained by the logical operator “and”.

For example, the combination of the conditions “A” and “B” for which the condition of logical sum (A or B) is “true” is “(A, B) = ( True, false), (false)
, True). Further, the combination of the conditions “A” and “B” for which the condition of logical sum (A or B) is “false” is as follows: (A, B) = ( It becomes one of false (false) and false (false).

Therefore, by applying the combination of the conditions “A” and “B” that make the condition of logical sum (A or B) “true” (A, B, C) = (true Two combinations of true / false values are generated: (true), false (true), true (false), true (true), true (true). Similarly, (A, B, C) = (true, false, false), (false, true, false) A combination of false values is generated.

In addition, by applying a combination of the conditions “A” and “B” that are “false” as the logical sum condition (A or B), (A, B, C) = (false (False), false (false), true (true) 1
A combination of street truth values is generated. Depending on the logical type of the logical operator that combines the conditions of the judgment expression, the truth value as a basic rule based on the logical product and logical sum illustrated in FIGS. 2A (1) and (2) is set for each condition. By applying it hierarchically, it is possible to obtain five combinations of truth values excluding redundancy.

In addition, when the basic rule by logical product or logical sum is applied, there are five true / false values for the three condition numbers (A, B, C) of the judgment expression “(A or B) and (C)”. Can be obtained. However, when the number of conditions is three, there are four combinations of the true / false values of the minimum conditions, so there is still one surplus of true / false combinations. Next, the truth for each condition in consideration of the coverage rule 4 of the MC / DC test coverage that can eliminate the combination of surplus truth values left when the basic rules based on logical product and logical sum are applied. A false value combination rule will be described.

(Combination rule)
FIGS. 2B (1) and 2 (2) illustrate three true / false relational relationships that satisfy the MC / DC test coverage for logical product (and) and logical sum (or). FIG. 2B (1) is an explanatory diagram of the true / false value relationship for the logical product, and FIG. 2B (2) is an explanatory diagram of the true / false value relationship for the logical sum.

As shown in the hatched area in FIG. 2B (1) and (2), the conditions of each of the conditions that affect the determination result independently in the logical product (and) and logical sum (or). There are boolean combinations. Here, the combination of true / false values of each condition that affects the determination result independently means that the true / false value as the determination result is changed when the true / false value for each condition is inverted. A combination of boolean values.

For example, assuming the logical product judgment expression “A and B”, as shown in the record of ID = 1 in FIG. 2B (1), the condition “A” is “true” and the condition “B” is “ In the combination of true / false values of “false”, the determination result (A and B) is “false”. For example, when the condition “A” is changed from “true” to “false” in the combination of true / false values of the record with ID = 1, the determination result becomes “false” and the change is made. The truth value does not change before and after.

On the other hand, when the condition “B” is changed from “false” to “true” in the combination of the truth values of the record of ID = 1, the determination result is “true”. ", And the true / false value changes before and after the change of the true / false value of the condition" B ". That is, the ID of FIG. 2B (1)
In the combination of the true / false values of the record = 1, the true / false value of the condition “B” can independently affect the true / false value of the determination result.

  In the record of ID = 2 in FIG. 2B (1), the truth value of the condition “A” in the combination of the condition “A” is “true” and the condition “B” is “false”. It can be said that the truth value of the determination result is influenced independently. In the combination of the conditions “A” and “B” for the record of ID = 2, if the condition “A” is changed from “false” to “true”, the determination result ( This is because the truth value of A and B) is changed from “false” to “true”.

In the combination of the record of ID = 3 in FIG. 2B (1) where the condition “A” is “true” and the condition “B” is “true”, the condition “A”, the condition It can be said that the truth value of “B” independently affects the truth value of the determination result. For example, the condition “A” is changed from “true” to “false” in the case of a combination of the conditions “A” and “B” of the record of ID = 3.
This is because the true / false value of the determination result (A and B) is changed from “true” to “false”. Similarly, for example, the condition “B” Even if the value is changed from “true” to “false”, the truth value of the judgment result (A and B) is changed from “true” to “false” Because.

In the logical sum judgment formula (A or B) in FIG. 2B (2), for example, as shown in the record of ID = 1, the condition “A” is “true” and the condition “B” is “false ( false) ”, the determination result (A or B) is“ true ”. For example, when the condition “A” is changed from “true” to “false” in the combination of the truth value of the record with ID = 1, the determination result is “false”. The true / false value changes before and after the change of the true / false value of the condition “A”. In the combination of the truth values of the record with ID = 1, the truth value of the condition “A” is
It can be said that the truth value of the determination result is influenced independently.

In the record of ID = 2 in FIG. 2B (2), the condition “A” is “false” and the condition “B” is “
It can be said that the true / false value of the condition “B” in the combination of “true” also affects the true / false value of the determination result independently. The conditions “A” and “B” of the record with ID = 2 When the condition “B” is changed from “true” to “false”, the truth value of the judgment result (A or B) is “true”. ) ”→“ false ”.

In the record with ID = 3 in FIG. 2B (2), the condition “A” is “false” and the condition “B” is “
In the combination of “false” values, it can be said that the truth values of the condition “A” and the condition “B” each independently affect the truth value of the determination result. For the combination of true / false values of record conditions “A” and “B” = 3, if the condition “A” is changed from “false” to “true”, the determination result (A or B) is changed from “false” to “true.” Similarly, for example, the condition “B” is changed from “false” to “true”. This is because the true / false value of the determination result (A or B) is also changed from “false” to “true” even when it is changed to “)”.

As illustrated in FIGS. 2B (1) and 2 (2), the combination of truth values by logical product (and) and logical sum (or) as basic rules is independent of the truth value of the determination result. There are combinations that affect it. Therefore, for example, by applying the combination rules 1 and 2 defined below to the combination of the truth values of the hierarchical (recursive) conditions of the basic rule, It is possible to obtain a truth value relationship.
Combination rule 1: When the true / false value as the determination result is not affected independently (the determination result does not change even if the true / false value of the target condition is inverted), the true / false value of the determination result is common. One combination is extracted from the combination of conditions and applied.
Combination rule 2: a combination of conditions in which the true / false value of the determination result is common when the true / false value as the determination result is influenced independently (if the true / false value of the target condition is inverted, the determination result changes) Are extracted and applied.

The combination rules 1 and 2 described above can be associated in advance with, for example, a truth table of logical products (and) and logical sums (or) as basic rules. For example, in the example of FIGS. 2B (1) and (2), it is only necessary to associate that the combination rule 2 is applied to the true / false value of the area hatched with the upward-sloping diagonal line. In the example of FIGS. 2B (1) and (2), for example, the combination rule 1 may be associated with a true / false value other than the region to which the combination rule 2 is applied.

Next, application of a combination of true / false values for each condition when the above-described combination rules 1 and 2 are reflected will be described. FIGS. 2C (1) and 2 (2) illustrate explanatory diagrams about hierarchical (recursive) application of the basic rule reflecting the above combination rule. In FIG. 2C (1) and (2), the determination formula is “(A or B) and C”.

The truth table table illustrated in FIG. 2C (1) is, for example, a condition when the basic rule of the logical product of FIG. 2A (1) is applied to the determination expression “(A or B) and C”. It shows the truth value relationship between “(A or B)” and condition “C”. As described with reference to FIG. 2A (1), the condition “(A or B)” is obtained by applying a logical AND basic rule to the judgment expression “(A or B) and C”.
, Three combinations of true / false values that satisfy the MC / DC test coverage for “C” are generated.

As illustrated in FIG. 2C (1), for the conditions “(A or B)” and “C”, ((A or B), C) = (true (true), false (false)), (false Three combinations of true / false values are generated: (false), true (true), and (true), true. Then, for each combination of the three true / false values, by applying the combination of the true / false values illustrated in FIG. 2A (2) to the condition “(A or B)”, the hierarchy of the basic rules A test case that reflects a combination of true (recursive) truth values is generated.

That is, when the condition (A or B) is set to “true” with respect to the three combinations of true / false values generated for the conditions “(A or B)” and “C”, the logical sum “ A combination of the conditions “A” and “B” illustrated in FIG. 2A (2) in which “A or B” is “true” may be applied. Similarly, if the condition (A or B) is “false” for the three combinations of true / false values generated for the conditions “(A or B)” and “C”, the logic The combination of the conditions “A” and “B” illustrated in FIG. 2A (2) in which the sum “A or B” becomes “false” may be applied.

Conditions of “A” and “B” combined by logical sum for the combination of three true / false values generated for conditions “(A or B)” and “C” combined by logical product By applying the combination of values, a test case based on a hierarchical (recursive) combination of truth values of the basic rule can be generated.

In the combination of the conditions illustrated in FIG. 2C (1), the condition “(A or or
2) “true”, which is the true / false value of “B”, does not independently affect the true / false value of the logical product determination result, as described in FIG. For B) ", when the basic rule of logical sum is applied hierarchically (recursively), the combination rule 1 may be adopted. That is, ID = 1 or ID illustrated in FIG. 2A (2) A combination of true / false values for the conditions “A” and “B” where the true / false value of the logical sum “A or B” is “true” may be applied.

Next, in each combination of conditions illustrated in FIG. 2C (1), “false”, which is the truth value of the condition “(A or B)”, of the record with ID = 2 is shown in FIG. As described in (3), the truth value of the logical product determination result is influenced independently. Therefore, for the condition “(A or B)”, the combination rule 2 may be adopted when applying the basic rule of logical sum hierarchically (recursively). That is, the truth value of the logical sum “A or B” with ID = 3 illustrated in FIG. 2A (2) is “false”.
All combinations of true / false values for the conditions “A” and “B” that are “)” may be applied.

In the example of FIG. 2A (2), the combination of the conditions “A” and “B” where the truth value of the logical sum “A or B” is “false” is ID = 3. This is one example. Therefore, in the hierarchical (recursive) application of the basic rule of logical sum to “false” of the condition “(A or B)” of the record of ID = 2 in FIG. 2C (1), FIG. A combination of true and false values for one condition “A” and “B” illustrated in ID = 3 in (2) is employed.

Next, in the combinations of the conditions illustrated in FIG. 2C (1), the condition “
“True”, which is a true / false value of “(A or B)”, independently affects the true / false value of the logical product determination result, as described with reference to FIG. When the basic rule of logical sum is applied hierarchically (recursively) to the record condition “(A or B)” = 3, the combination rule 2 may be adopted, for example, FIG. All the combinations of truth values for the conditions “A” and “B” in which the truth value of the logical sum “A or B” is “true” with the ID = 1, 2 shown in FIG. do it.

In FIG. 2A (2), the combination of the truth values of the conditions “A” and “B” where the truth value of the logical sum “A or B” is “true” is exemplified by ID = 1 ( True, false), and ID =
There are two examples (false (false) and true (true)). Therefore, the ID of FIG. 2C (1)
= 3, in the hierarchical (recursive) application of the basic rule of logical sum to “true” of the condition “(A or B)”, ID = 1, 2 in FIG. 2A (2) Two conditions “A” illustrated in
A combination of true / false values for “B” is adopted.

FIG. 2C (2) reflects the combination rule for each record of the logical product illustrated in FIG. 2C (1) and hierarchically (recursively) sets the truth value for the logical sum “(A or B)”. It is an example of a truth table of a test case generated by applying to. The truth table table illustrated in FIG. 2C (2) has columns for the conditions “A”, “B”, and “C” included in the determination formulas “(A or B) and C”.

  The record with ID = 1 in FIG. 2C (2) corresponds to the record with ID = 1 in FIG. 2C (1). In the record of ID = 1 in FIG. 2C (2), there are two kinds of truth values “A” and “B” that make the truth value of the condition “(A or B)” “true”. (A, B) = (true, false) extracted from the combination is stored. As described in the combination rule 1, the combination of the true / false values of the conditions “A” and “B” stored in the record with ID = 1 in FIG. 2C (2) is, for example, the other true / false value. (A, B) = (false, true).

The record with ID = 2 in FIG. 2C (2) corresponds to the record with ID = 2 in FIG. 2C (1). In the record of ID = 2 in FIG. 2C (2), the true / false value of the condition “(A or B)” is set to “false”.
(A, B) = (false, false), which is a combination of one true / false value of conditions “A” and “B”.

The records with ID = 3_1 and 3_2 in FIG. 2C (2) correspond to the records with ID = 3 in FIG. 2C (1). In the record of ID = 3_1 in FIG. 2C (2), there are two true / false values of conditions “A” and “B” in which the true / false value of the condition “(A or B)” is “true”. (A, B) = (
True (false)) is stored. In addition, the record with ID = 3_2 in FIG. 2C (2) has two true conditions “A” and “B” that set the true value of the condition “(A or B)” to “true”. Among the combinations of false values, (A, B) = (false, true) is stored.

  As described above, in the first embodiment, the basic rule of logical product and logical sum reflecting the combination rule is hierarchically (recursively) for the combination of the true / false values of each condition included in the determination formula to be tested. The minimum number of test cases satisfying the coverage rule 1-4 is generated. In the first embodiment, for example, it is possible to eliminate a combination of surplus / false values of each condition, which is left as a combination of hierarchical (recursive) truth values of basic rules of logical product and logical sum. It becomes.

For example, in the basic rule of the logical sum illustrated in FIG. 2A (2), the truth values of the conditions “A” and “B” where the true value of the logical sum (A or B) is “true” are set. The combination is (A, B) = (true)
There are two types: false (false) and (false), true. Here, as illustrated in FIG. 2C (1), in the basic rule of the logical product on the condition of the logical sum (A or B), the logical sum (A or B)
) If the truth value of the other party's condition that combines the truth value of “) is“ false ”, the truth value of the logical sum (A or B) is independent of the truth value of the judgment result. It does not affect. For this reason, the combination rule 1 is applied to the combination of the conditions “A” and “B” with the truth value of the logical sum (A or B) being “true”. .

When combination rule 1 is applied, one combination is extracted from a combination of conditions with the same true / false value of the determination result, so the true / false value of condition “(A or B)” is set to “true” ) "Is extracted from one of the two combinations of true / false values of conditions" A "and" B ". As a result, for example, as illustrated in the record of ID = 1 in FIG. 2C (2), as a combination of the truth values of the conditions “A” and “B”, (A, B) = (true), False) is extracted and the other (A, B) =
It becomes possible to exclude a combination of true / false values (false, true). When (A, B) = (false (true), true) is extracted as a combination of truth values of the conditions “A” and “B”, (A, B) = ( True (false)) will be excluded as a combination of surplus boolean values.

  In the first embodiment, when the basic rules of logical product and logical sum are applied hierarchically (recursively), the combination rules are reflected to satisfy the coverage rule 1-4 for each condition included in the determination formula. It becomes possible to obtain a combination of the minimum number of true / false values. However, for example, when a combination rule 1 is reflected, when one combination is extracted from a combination of conditions with the same truth value of the determination result, a contradiction may occur depending on the combination of the truth values for each extracted condition. May contain.

FIG. 3 illustrates an explanatory diagram of a combination of true and false values for each condition when a contradiction occurs, taking the combination rule 1 as an explanatory example. The judgment formula in the explanation example is “((x> 10) or (y == 0)) and
(x> 0) ”. In the judgment formulas“ ((x> 10) or (y == 0)) and (x> 0) ”, the conditions“ (x> 10) ”,“ (y == 0 ) ”Is a lower-level processing process, and the condition“ (x> 10) ”,“ (y == 0) ”or“ ((x> 10) or (y == 0)) ” AND of the condition “(x> 0)” is the upper processing process.

As illustrated in the record of ID = 1-3 in FIG. 3, the logical sum “((x> 10) or (y == 0))” and the condition “(x> 0) of“ (y == 0) ”. ) ”And apply the basic rule to the logical AND and 3 for each condition
A combination of street truth values is determined.

In the record of ID = 1, the logical sum “((x> 10) or (y == 0))” and the condition “(x> 0)” and the condition “(x> 0)” are combined. 10) or (y == 0)) ”is set to“ true ”, the combination rule 1 applies to the combination of the truth values“ (x> 10) ”and“ (y == 0) ” Is done. Therefore, for example, from the combination of truth values of the logical sum as the basic rule illustrated in FIG. 2B (2), either one of ID = 1 or ID = 2 is changed to the logical sum “((x> 10) or (y == 0)) ”to“ true
) ”May be applied to a combination of truth values of“ (x> 10) ”and“ (y == 0) ”.

  When the basic rule of the logical sum of ID = 1 in FIG. 2B (2) is adopted as a combination of the truth values of the conditions “(x> 10)” and “(y == 0)”, the condition “(x> 10 ) ”Is“ true ”, and the truth value of the condition“ (y == 0) ”is“ false ”. Here, it is assumed that a condition in which the true / false value is “false” is represented by adding “not” indicating logical negation to the corresponding condition.

When the basic rule of the logical sum of ID = 1 in FIG. 2B (2) is adopted as a combination of the truth values of the conditions “(x> 10)” and “(y == 0)”, the condition “(x> 10 ) ”,“ (Y == 0) ”is a combination expression“ (x> 10)
and not (y == 0) ”.

Since the truth value for the condition “(x> 0)” in the record of ID = 1 in FIG. 3 is “false”, “not” indicating logical negation is added to the condition “(x> 0)”. It can be expressed as the condition “not (x> 0)”. Therefore, the combination of the conditions “(x> 10)”, “(y == 0)”, “(x> 0)” reflecting the combination rule 1 for the example true / false value of ID = 1 in FIG. Can be expressed as the following bond formula (A1).
-Joining formula: (x> 10) and not (y == 0) and not (x> 0) ... Joining formula (A1)
Since the join expression (A1) includes the conditions “(x> 10)” and “not (x> 0)” at the same time, the variables common to the conditions “(x> 10)” and “not (x> 0)” There will be a contradiction for x. Therefore, a test set (a combination of variable values that satisfies each condition) that satisfies the combination formula (A1) that reflects a combination of true and false values generated as a test case that satisfies the test coverage of MC / DC is obtained. It becomes impossible.

On the other hand, when the basic rule of the logical sum of ID = 2 in FIG. 2B (2) is adopted as a combination of the truth values of the conditions “(x> 10)” and “(y == 0)”, the condition “(x > 10) ”is“ false ”, and the truth value of the condition“ (y == 0) ”is“ true ”. Therefore, the truth value is “false”
In addition, “not” indicating logical negation is added to the condition, and the combination of the conditions “(x> 10)” and “(y == 0)” of ID = 2 in FIG. Then, the combined expression “not (x> 10) and (y == 0)”.

When the logical sum of ID = 2 in FIG. 2B (2) is adopted, the conditions “(x> 10)”, “(y) reflecting the combination rule 1 with respect to the true / false value illustrated as ID = 1 in FIG. == 0) ”and“ (x> 0) ”can be expressed as the following coupling equation (A2).
・ Combination formula: not (x> 10) and (y == 0) and not (x> 0) ... coupling formula (A2)
In the combination formula (A2), the conditions “not (x> 10)” and “not (x> 0)” are included at the same time.
There is no contradiction in the variable x common to the conditions “not (x> 10)” and “not (x> 0)”. For this reason, it becomes possible to obtain a test set that satisfies the combined equation (A2) reflecting the combination of true / false values generated as test cases satisfying the test coverage of MC / DC.

  In this way, if the judgment formula to be tested includes multiple conditions that share a variable, the combination of the conditions to which the combination rule, the logical product of the basic rules, and the truth value of the logical sum are applied On the other hand, there is a possibility that a contradiction arises in the combination of the variable values satisfying the truth value. Even if it is a true / false value generated as a test case that satisfies the test coverage of MC / DC, if the true / false value is reflected in each condition, it is impossible to obtain a variable value that satisfies each condition. May end up.

  For example, when the combination rule 1 is applied and a combination of true and false values of the logical sum of ID = 1 illustrated in FIG. 2B (2) is extracted, the coverage rule 1-4 of the MC / DC test coverage is used. It is impossible to obtain a satisfactory solution for each condition reflecting the true / false relation of as a test set. On the other hand, when a combination of true / false values of the logical sum of ID = 2 illustrated in FIG. 2B (2) is extracted when the combination rule 1 is applied, the coverage rule 1-4 of the MC / DC test coverage is obtained. It is possible to obtain a satisfactory solution for each condition reflecting the true / false relation of as a test set.

<Example 2>
In the first embodiment, in the combination rule 1, the information processing apparatus does not affect the truth value as the determination result independently (the determination result does not change even if the truth value of the target condition is inverted). Then, one combination was extracted from the combination of conditions having the same true / false value of the determination result and applied. Now, for example, it is assumed that a combination of conditions that do not affect the true / false values independently and share the true / false values of the determination results is the combination A and the combination B.

  For example, when the combination rule 1 is applied, the processing in the information processing apparatus according to the second embodiment holds a combination of conditions (combination A and combination B) in which the truth value of the determination result is common in the combination rule 1. Introducing a selection operator for

By using a selection operator, the information processing apparatus according to the second embodiment can hold and extract a combination of true / false values of each condition having the same true / false value as a determination result in a combined state by the selection operator. It becomes possible. Then, the processing of the information processing apparatus according to the second embodiment is based on the combination of true / false values of the conditions based on the logical operator of the highest level processing process, and is extracted in a state of being combined with the selection operator. The satisfiability with respect to each variable is determined for the combination of the true and false values of each condition in the process. The processing of the second embodiment other than using the selection operator is the same as that of the first embodiment.

FIG. 4 illustrates an explanatory diagram for extracting a combination of true / false values of each condition from a lower-level processing process of the second embodiment using the combination rule 1 as an explanatory example. Note that the determination formulas in the explanation example are the same as those in the explanation example of FIG. 3, that is, “((x> 10) or (y == 0)) and (x> 0)”. Judgment formula “((x> 10) or
In (y == 0)) and (x> 0) ”, the logical sum of the conditions“ (x> 10) ”and“ (y == 0) ”is a lower processing process, and the condition“ (x> 10) ”,“ (y == 0) ”or“ ((x> 10) or (y == 0)) ”and the condition“ (x> 0) ” is there.

In the record of ID = 1 shown in the block P5 of FIG. 4, in the combination of the truth value of the logical sum “((x> 10) or (y == 0))” and the condition “(x> 0)”, Combination of the truth values "(x>10)" and "(y == 0)" that make the logical sum "((x> 10) or (y == 0))""true" Combination rule 1 is applied to.

  In the first embodiment, for example, the combination of the truth value of either ID = 1 or ID = 2 is extracted from the combination of the truth values of the logical sum as the basic rule illustrated in FIG. 2B (2). Is done. For this reason, depending on the combination of the truth values of the extracted conditions “(x> 10)” and “(y == 0)”, the test set (each condition There is a possibility that it may become impossible to obtain a combination of variable values satisfying the above.

  In the processing in the second embodiment, for example, the logical sum “((x> 10) or (y == 0))” is changed to “true” from the combination of the logical values of the logical sum illustrated in FIG. The combination of the true / false values of ID = 1 and 2 under the conditions “(x> 10)” and “(y == 0)” are combined with the selection operator “||” and extracted. For example, for a condition where the true / false value is “false”, adding “not” indicating logical negation reflects the true / false value, and ID = 1 in which the true / false value is reflected, A combination of the two conditions is combined with a selection operator “||” to generate a subexpression. In the second embodiment, as shown in a block P6 in FIG. 4, a partial expression generated by combining a combination of true / false values of ID = 1, 2 with a selection operator “||” is extracted.

For example, as described with reference to FIG. 3, “(x> 10) and not (y == 0)” is generated from the combination of the truth values of ID = 1, and from the combination of the truth values of ID = 2. Produces “not (x> 10) and (y == 0)”. In the second embodiment, for example, a combination of conditions reflecting a true / false value of ID = 1 and a combination of conditions reflecting a true / false value of ID = 1 are selected using a selection operator “||”. The combined subexpression (A3) is generated.
Sub-expression: ((x> 10) and not (y == 0)) || ((not (x> 10) and (y == 0)) ... sub-expression (A3)
In the sub-expression (A3), the conditions “(x> 10)”, “(y == 0) where the logical sum“ ((x> 10) or (y == 0)) ”is“ true ”are set. All combinations of Boolean values of “” are expressed as selection relationships such as “((x> 10) and not (y == 0)) or ((not (x> 10) and (y == 0))” be able to.

In the processing in the second embodiment, the generated sub-expression (A3) is combined with the condition “(x> 0)” reflecting the true / false value in the upper processing process, and the upper / lower processing process is verified. A join expression (A4) based on the values is generated. In the record of ID = 1 shown in the block P5 of FIG. 4, the true / false value for the condition “(x> 0)” is “false”, so the condition reflecting the true / false value is “not” (X> 0) ”.
・ Combined expression: (((x> 10) and not (y == 0)) || ((not (x> 10) and (y == 0))) and not (X> 0) ... A4)
In the join expression (A4), the logical sum “((x> 10) or (y == 0))” of the record with ID = 1 shown in the block P5 of FIG. 4 is set to “true” and the condition “ Includes a combination of conditions “(x> 10)”, “(y == 0)”, “(X> 0)” reflecting a true / false value with “X> 0)” as “false” It is.

Then, in the processing in the second embodiment, as shown in block P7 of FIG. 4, the generated combination formula (A4) is expanded with respect to the selection operator, and the processing is performed in a lower level processing process combined by the selection operator. Sub-expressions (A5) and (A6) reflecting the true / false values of each condition are generated. In the block P7 of FIG. 4, the subexpression (A5) is expressed as “determination logical expression when case 1 is selected”, and the subexpression (A6) is expressed as “determination logical expression when case 2 is selected”. .
Sub-expression: ((x> 10) and not (y == 0)) and not (X> 0) ... sub-expression (A5)
Sub-expression: ((not (x> 10) and (y == 0)) and not (X> 0) ... sub-expression (A6)
In the sub-expression (A5), conditions “(x> 10)”, “(y == 0)”, “(X> 0) reflecting ID = 1 in ID = 1 in FIG. 2B (2) ”Is included. Further, in the partial expression (A6), FIG.
2) includes a combination of conditions “(x> 10)”, “(y == 0)”, and “(X> 0)” reflecting an example true / false value in ID = 2.

  In the second embodiment, for example, with respect to the sub-expressions (A5) and (A6) generated by expanding the selection operator, it is determined whether or not there is a variable value that can satisfy the combination of the conditions reflecting the true / false values. The Then, as shown in a block P8 in FIG. 4, a partial expression is selected from which a variable value satisfying a combination of conditions reflecting the true / false values is obtained. In the second embodiment, a test set that satisfies the coverage rule 1-4 for the MC / DC test coverage is obtained based on the selected sub-expression.

  For example, in the sub-expression (A5), since the range relation about the variable x includes a contradiction, it is determined that a variable value that satisfies each combination of conditions that reflects a true / false value cannot be obtained (unsatisfiable). On the other hand, in the sub-expression (A6), since the range relation regarding the variables x and y does not include any contradiction, it is determined that a variable value satisfying the combination of the conditions reflecting the true / false values can be obtained (satisfiable). . In the processing of the second embodiment, for example, the subexpression (A6) determined to be satisfiable is selected, and the coverage of the MC / DC test coverage is based on the combination of the conditions of the selected subexpression (A6). A test set that satisfies Rule 1-4 is required.

  When it is determined that each sub-expression generated by expanding the selection operator is satisfiable, for example, a sub-expression based on the combination of the leftmost conditions combined by the selection operator is You may choose. When there are a plurality of sub-expressions that are determined to be satisfiable, a method for selecting a sub-expression to be selected may be determined in advance.

  In the second embodiment, the satisfiability with respect to the combination of the truth values of the conditions extracted from the lower-level processing processes in the state of being connected by the selection operator is the combination of the truth values of the conditions in the highest-level processing process. Can be determined based on Therefore, for example, a combination of true / false values of each condition that does not cause a contradiction can be selected from combinations of true / false values of each condition in a lower processing process to which the combination rule 1 is applied. As a result, the second embodiment can provide a technique for generating a test case that satisfies the coverage rule of the MC / DC test coverage that improves the satisfiability and can eliminate redundancy.

〔Device configuration〕
FIG. 5 illustrates a hardware configuration of the information processing apparatus according to the second embodiment (hereinafter referred to as the present embodiment). The information processing apparatus 10 illustrated in FIG. 5 is a computer such as a PC (Personal Computer) or a server, for example. The information processing apparatus 10 includes a central processing unit (CPU) 11, a main storage unit 12, an auxiliary storage unit 13, an input unit 14, an output unit 15, and a communication unit 16 that are connected to each other via a connection bus B <b> 1. The main storage unit 12 and the auxiliary storage unit 13 are recording media that can be read by the information processing apparatus 10.

In the information processing apparatus 10, the CPU 11 develops a program stored in the auxiliary storage unit 13 so as to be executable in the work area of the main storage unit 12, and controls peripheral devices through the execution of the program. Thereby, the information processing apparatus 10 can implement a function that matches a predetermined purpose.

  In the information processing apparatus 10 illustrated in FIG. 5, the CPU 11 is a central processing unit that controls the entire information processing apparatus 10. The CPU 11 performs processing according to a program stored in the auxiliary storage unit 13. The main storage unit 12 is a storage medium in which the CPU 11 caches programs and data and expands a work area. The main storage unit 12 includes, for example, a RAM (Random Access Memory) and a ROM (Read Only Memory).

  The auxiliary storage unit 13 stores various programs and various data in a recording medium in a readable and writable manner. The auxiliary storage unit 13 is also called an external storage device. The auxiliary storage unit 13 stores an operating system (OS), various programs, various tables, and the like. The OS includes a communication interface program that exchanges data with an external device or the like connected via the communication unit 16. Examples of the external device include an information processing device such as another server, an external storage device, and a device having a communication function on a network connected via the communication unit 16.

The auxiliary storage unit 13 is, for example, an EPROM (Erasable Programmable ROM), a solid state drive device, a hard disk drive (HDD, Hard Disk Drive) device, or the like. Further, as the auxiliary storage unit 13, for example, a CD drive device, a DVD drive device, a BD (Blu-ray (registered trademark) Disc) drive device, or the like can be presented. Examples of the recording medium include a silicon disk including a nonvolatile semiconductor memory (flash memory), a hard disk, a CD, a DVD, a BD, a USB (Universal Serial Bus) memory, and a memory card.

  The input unit 14 receives an operation instruction or the like from a user or the like. The input unit 14 is an input device such as an input button, a pointing device such as a keyboard and a touch panel, a wireless remote controller, a microphone, and a camera. Information input from the input unit 14 is notified to the CPU 11 via the connection bus B1.

The output unit 15 outputs data processed by the CPU 11 and data stored in the main storage unit 12. The output unit 15 is an output device such as a CRT (Cathode Ray Tube) display, an LCD (Liquid Crystal Display), a PDP (Plasma Display Panel), an EL (Electroluminescence) panel, an organic EL panel, a printer, or a speaker. The communication unit 16 is an interface with a network or the like, for example. The network to which the information processing apparatus 10 is connected includes, for example, a public network such as a LAN (Local Area Network) and the Internet, and a wireless network such as a mobile phone network including a communication base station.

  In the information processing apparatus 10, for example, the CPU 11 reads out the OS, various programs, and various data stored in the auxiliary storage unit 13 to the main storage unit 12 and executes them. Each processing means is realized. The information processing apparatus 10 implements the determination formula analysis unit 101, the true / false value rule application unit 102, the test case generation unit 103, and the satisfiability determination unit 104 illustrated in FIG. 5 together with the execution of the target program. The judgment formula analysis unit 101 includes a binary tree conversion unit 101a, and the test case generation unit 103 includes a selection analysis unit 103a. However, any of the processing means illustrated in FIG. 5 or a part of them may be operated by a hardware circuit.

Further, the information processing apparatus 10 includes, for example, a truth value rule DB 201, a judgment expression binary tree DB 202, and a conditional expression map set DB 203 as auxiliary storage units as storage destinations of data to be referred to or managed by the above processing units. 13 ready. In addition, the information processing apparatus 10 includes the auxiliary storage unit 13.
In addition, a test case conditional expression DB 204 and a test case DB 205 are provided.

  Any one of the processing means may be included in another information processing apparatus or the like. For example, the information processing apparatus including the determination formula analysis unit 101, the information processing apparatus including the truth value rule application unit 102, the information processing apparatus including the test case generation unit 103, and the information processing including the satisfiability determination unit 104 The device is connected via a network or the like. Each information processing apparatus connected via the network may function as the information processing apparatus 10. Similarly, the truth value rule DB 201, the judgment expression binary tree DB 202, the conditional expression map set DB 203, the test case conditional expression DB 204, and the test case DB 205 of the information processing apparatus 10 are distributed and stored in a plurality of external storage devices. Or the like. Since the information processing apparatus 10 can be realized as, for example, a cloud that is a group of computers on a network, the processing load of each processing unit can be reduced.

[Process block configuration]
FIG. 6 illustrates an explanatory diagram of processing blocks in the information processing apparatus 10 of the present embodiment. In the explanatory diagram illustrated in FIG. 6, the information processing apparatus 10 includes processing units of a determination formula analysis unit 101, a truth value rule application unit 102, a test case generation unit 103, and a satisfiability determination unit 104. The judgment formula analysis unit 101 includes processing means of the binary tree conversion unit 101a. The test case generation unit 103 includes processing means of the selection analysis unit 103a.

  In addition, in the explanatory diagram illustrated in FIG. 6, the information processing apparatus 10 refers to, for example, a truth value rule DB 201, a judgment expression binary tree DB 202, as a storage destination of data to be referred to or managed by the above functional units. The conditional expression map set DB 203 is provided in the auxiliary storage unit 13. In addition, the information processing apparatus 10 includes a test case conditional expression DB 204 and a test case DB 205 in the auxiliary storage unit 13.

  The determination formula analysis unit 101 illustrated in FIG. 6 receives, for example, a determination formula (logical formula) C1 of a program to be tested. For example, the determination formula analysis unit 101 reads the test target program stored in a recording medium such as a CD or a USB memory via the auxiliary storage unit 13 to receive the determination formula C1 of the program. it can. In addition, the determination formula analysis unit 101 reads the test target program stored in another information processing apparatus, an external storage device, or the like via, for example, a network to which the information processing apparatus 10 is connected. The determination formula C1 described in the program may be accepted. For example, the determination formula analysis unit 101 temporarily stores the determination formula C1 received by the information processing apparatus 10 in a predetermined area of the main storage unit 12. The program to be tested may be a source program or a binary program. The determination formula C1 may be a logical expression in text format or a binary code.

  The judgment formula analysis unit 101 analyzes the judgment formula C1 of the received program, and determines the logical operator included in the judgment formula C1, each condition linked by the logical operator, and the hierarchical relationship of the processing processes of each condition by the logical operator. Identify. In the following description, the conditions included in the determination formula are also referred to as “conditional formulas”.

  The determination expression analysis unit 101 generates a tree structure (determination expression binary tree) for the determination expression of the program to be tested based on the identified logical operator, each conditional expression, and the hierarchical relationship of the processing processes. Note that the logical operator included in the judgment expression, each conditional expression combined by the logical operator, and the processing for specifying the hierarchical relationship of the processing process of each conditional expression by the logical operator are performed by, for example, the binary tree of the judgment expression analysis unit 101 This is performed by the conversion unit 101a. The tree structure (judgment binary tree) of the judgment formula generated by the judgment formula analysis unit 101 is stored in the judgment formula binary tree DB 202, for example.

[Binary tree conversion processing]
The binary tree conversion unit 101a of the determination expression analysis unit 101 analyzes the received determination expression C1, and includes logical operators included in the determination expression B1, conditional expressions combined by logical operators, and conditional expressions based on logical operators. Identify the hierarchical relationship of the processing processes for. Then, the binary tree conversion unit 101a converts the hierarchical logical operation relationship of each condition related to the determination of the received determination expression C1 into a tree structure based on the identified logical operator, each conditional expression, and the hierarchical relationship of the processing processes. Convert.

  FIG. 7A illustrates an explanatory diagram of binary tree conversion processing. In addition, the determination formula C1 related to the binary tree processing in the explanatory diagram illustrated in FIG. 7A is “(((x> 0) and (z == 5)) or ((y == 1) illustrated in FIG. ) and (z> 1))) and (y> 0) ”.

  The binary tree conversion unit 101a performs, for example, linguistic analysis, lexical analysis, syntax analysis, and the like to delimit parentheses such as “(”, “)” included in the determination formula C1 or an area such as a space. To detect. Then, the binary tree conversion unit 101a identifies the character string of the logical operator and the variable conditional expression included in the partitioned area range.

For example, the binary tree conversion unit 101a sets “x> 0”, “z == 5”, “y =” for a variable from an area range delimited by parentheses such as “(”, “)”. = 1 ”,“ z> 1 ”,“ y> 0 ”are specified. In addition, for example, the binary tree conversion unit 101a performs “)”, “(”
A character string of a logical operator such as “or” or “and” is specified from an area range delimited by an array of parenthesis symbols such as.

  Further, the binary tree conversion unit 101a specifies the priority of the logical operation related to the determination based on the arrangement relationship of the parenthesis symbols such as “(”, “)”, and processes the logical operator having the higher priority to the lower level processing. Identify the hierarchical relationship between logical operators as a process. Then, the binary tree conversion unit 101a converts the hierarchical logical relationship of each conditional expression into a tree structure from the correspondence relationship between the logical relationship between the logical operators and the conditional expression linked by the logical operator. Convert.

  However, with respect to a determination expression in which “(”, “)”, etc. are omitted and a plurality of logical operators having the same priority are arranged, the binary tree conversion unit 101a is, for example, the same as a programming language that describes a program. What is necessary is to interpret the priority. In the binary tree conversion unit 101a, for example, “(”, “)” or the like is omitted, and a determination expression in which a plurality of logical operators having the same priority is arranged can be given priority to the left side, for example.

  In the example of FIG. 7A, the conditional expressions “x> 0”, “z == 5”, “y == 1”, “z> 1” are arranged in the lowest layer of the tree structure. And the logical operator “and” that connects the conditional expressions “x> 0” and “z == 5” and the logical operator “and” that connects the conditional expressions “y == 1” and “z> 1” Are arranged in the same hierarchy.

  Of the two “ands” arranged in the same hierarchy in FIG. 7A, the left logical operator “and” is associated with the conditional expressions “x> 0” and “z == 5”. Similarly, the logical operator “and” on the right side is associated with the conditional expressions “y == 1” and “z> 1”.

In addition, a processing process including conditional expression “y> 0”, conditional expression “x> 0”, “z == 5”, and a processing process including conditional expressions “y == 1”, “z> 0” The logical operator “or” that ties is arranged in the same hierarchy. And the processing process by the logical operator “or” and the conditional expression “y> 0”
It is linked by the top logical operator “and” in the tree structure.

The logical operator “or” includes the conditional operator “x> 0”, the logical operator “and” of the processing process including “z == 5”, and the conditional expressions “y == 1”, “z> It is associated with the logical operator “and” of the processing process including “1”. Further, the highest logical operator “and” is associated with the logical operator “or” and the conditional expression “y> 0”.

  As illustrated in FIG. 7A, the determination formula converted into a tree structure can represent a hierarchical relationship between logical operators and a correspondence relationship between the conditional expressions linked by the logical operators. In the example of the tree structure in FIG. 7A, it can be seen that the logical operators and the conditions included in the determination formula are arranged in order from the left side. Further, in the tree structure generation process of FIG. 7A, it can be seen that a process process with a higher priority on the inner side surrounded by the symbol “()” is arranged in the lower layer. It can also be seen that the left logical operation relationship described in the logical expression is arranged on the left side of the tree structure. Here, a conditional expression represented by a tree structure is also referred to as a “leaf”, and a logical operator that connects each “leaf” is also referred to as a “node”.

  The binary tree conversion unit 101a uniquely identifies the hierarchical relationship between logical operators and the correspondence relationship of each conditional expression linked by the logical operator in the determination equation converted into a tree structure. The identification number is stored in the determination formula binary tree DB 202 in association with the identification number.

[Boolean value rule application processing]
The truth value rule application unit 102 is, for example, a logical product or a logical sum of logical sums to which the combination rule is applied based on the tree structure (judgment binary tree) of the judgment formula generated by the judgment formula analysis unit 101. A combination of conditional expressions (leafs) reflecting the above is generated. Note that the truth value rule application unit 102, for example, in the combination of the logical product and the logical value of the logical sum to which the combination rule 1 is applied, all the conditional expressions that share the truth value in the upper processing process. Combinations of (leaf) boolean values are extracted by combining with the selection operator “||”.

  In the truth value rule application unit 102, for example, a combination rule based on the logical relationship between nodes, a combination of conditional expressions (leafs) reflecting the truth values of logical products and logical sums is generated for each hierarchy of processing processes. . Then, the truth value rule application unit 102, for example, based on the combination of conditional expressions (leafs) reflecting the truth value for each level of the processing process, as illustrated in FIG. A combined expression (A4) of each conditional expression (leaf) reflecting the true / false value corresponding to the process is generated.

  The truth value rule application unit 102 associates the combined expression generated by reflecting the truth value of the highest-level processing process with, for example, the identification number of the determination target judgment expression in the conditional expression map set DB 203. Store.

  Hereinafter, the truth value rule application processing of this embodiment will be described with reference to the drawings illustrated in FIGS. 7B-7H. In the truth value rule application process of the present embodiment, the truth value rule application unit 102 includes a “conditional expression map” in which a conditional expression and a truth value are associated with each leaf, and a “conditional expression map set”. Is generated.

  Here, the “conditional expression map” represents, for example, a combination with (conditional expression, true / false value). The “conditional expression map set” is a set of “conditional expression maps” expressed as {(conditional expression 1, truth value 1), (conditional expression 2, truth value 2)}, for example.

In the “conditional expression map”, leaves reflecting the paired true / false values are combined as a “conditional expression”. For example, when the true / false value of the pair is “true”, a combination such as “(leaf, true)” is generated as the conditional expression map. Further, for example, when the true / false value to be paired is “false”, the symbol “¬” representing negative logic (not) is reflected in the leaf, and the combination “(¬ (leaf), false)” Is generated as a conditional expression map. There are two types of “conditional expressions” in which the true / false relations are reflected on the leaves: “leaf” and “¬ leaf”.

The truth value rule application unit 102 combines, for each leaf, a conditional expression that reflects each of the true / false values “true” and “false”, and the reflected true / false value. Then, a conditional expression map for each truth value is generated. Then, the truth value rule application unit 102 generates, for example, a “conditional expression map set” for each leaf such as {(leaf, true), (¬ (leaf), false)} by combining the generated conditional expression maps. To do.

  The truth value rule application unit 102, for example, from the “conditional expression map set” generated for each leaf, the truth of the basic rule of the logical product or logical sum reflecting the combination rule of the nodes linking each leaf A “conditional expression map” corresponding to the value is extracted. Then, the truth value rule application unit 102 combines, for example, the “conditional expression map” extracted for each leaf, and combines the combined “conditional expression map” with the true / false value in the node processing process. Each “conditional expression map set” is generated.

  In the truth value rule application processing of the present embodiment, the truth value rule application unit 102 generates a “conditional expression map set” that reflects the truth value of the processing process described above for each layer of the processing process. Then, a “conditional expression map set” reflecting the truth value corresponding to the highest processing process is generated. Note that the “conditional expression map set” generated by reflecting the true / false value corresponding to the highest level processing process is, for example, each condition that reflects the true / false value of the logical product or logical sum to which the combination rule is applied. This is a combination of formulas (leafs), and is the coupling formula (A4) described in FIG.

(First Boolean value rule application process)
FIG. 7B illustrates an explanatory diagram of processing for generating a conditional expression map and a conditional expression map set for the tree structure generated from the determination expression C1. In the second embodiment, description will be made assuming that the generation of the conditional expression map and the conditional expression map set for each leaf is performed, for example, from the processing process of the node arranged on the left side of the tree structure. This is because when a plurality of nodes (logical operators) are arranged on the same hierarchy, for example, the processing process arranged on the left side can be given priority. However, the processing of the information processing apparatus is not limited to processing with priority given to the processing process arranged on the left side, and processing processing arranged on the right side may be given priority.

  In the first truth value rule application process, the truth value rule application unit 102, for example, a conditional expression map for node (logical operator) processing arranged on the left side of the same hierarchy of the tree structure, and Create a set of conditional expression maps.

  The truth value rule application unit 102 refers to, for example, the determination expression binary tree DB 202 and acquires a tree structure (determination expression binary tree) generated for the determination expression to be processed. Then, the truth value rule application unit 102 generates, for example, a “conditional expression map” and a “conditional expression map set” for each leaf for the processing process of the node arranged on the left side of the lower layer of the tree structure. .

In the tree structure illustrated in FIG. 7B, each leaf related to the processing process of the node arranged on the left side of the lower layer is represented by a leaf “x> 0”, “z ==”, as indicated by a rectangular region surrounded by a broken line. 5 ”.

For example, for the leaf “x> 0”, the truth value rule application unit 102 generates a conditional expression map ((x> 0), true) in combination with the truth value of “true”. A conditional expression map (¬ (x> 0), false) of a combination with a false value is generated, and the truth value rule application unit 102 generates, for example, a leaf “x> 0”. Each conditional expression map is combined to generate a conditional expression map set {((x> 0), true), (¬ (x> 0), false)} shown in the block D1.

Further, the truth value rule application unit 102, for example, for the leaf “z == 5”, “true”
Generate a conditional expression map (“(z == 5), true) with a true / false value of“ ”, and create a conditional expression map (¬ (z == 5) with a“ false ”true / false value. , False). Boolean value rule application unit 10
2 is a combination of the conditional expression maps generated for the leaf “z == 5”, for example, and the conditional expression map set {((z == 5), true), (¬ (z == 5)) shown in the block D1. , False)}.

The truth value rule application unit 102 temporarily stores the conditional expression map set generated for each of the leaves “x> 0” and “z == 5”, for example, in a predetermined area of the main storage unit 12. .

Next, the Boolean value rule application unit 102 calculates the Boolean value for the processing process of the node linking the leaves “x> 0” and “z == 5” based on the conditional expression map set generated for each leaf. Generate the reflected conditional expression map set.

FIG. 7C illustrates an explanatory diagram of the generation process of the conditional expression map set reflecting the true / false values for the processing processes related to the leaves “x> 0” and “z == 5”. In the explanatory diagram of FIG. 7C, the conditional expression map set generated for the leaf “x> 0” is also referred to as a “left-side conditional expression map set” as shown in the block D1, and the leaf “z == 5”. The conditional expression map set generated for is also referred to as a “right-side conditional expression map set”. In addition, the nodes and leaves that are the targets of the generation process are shown in a rectangular area surrounded by a broken line.

In the explanatory diagram of FIG. 7C, the logical product rule Tb1 represents a truth table of the logical product to which the combination rule is applied. In the logical product rule Tb1, for example, in the “left side” column and the “right side” column, truth value information such as “(true, 0)” and “(false, 1)” is stored. Here, for example, in the truth value information “(true, 0)”, “(false, 1)”, etc.
Numeric values such as “0” and “1” combined with true / false values “true” and “false” are identifiers that specify the combination rule to be applied.

  In the example of the logical product rule Tb1 in FIG. 7C, in the case of a truth value in which the identifier “0” is combined, the combination rule 1 is applied when combining the basic rules of logical product and logical sum, and the identifier “1” is For the combined truth value, the combination rule 2 is applied.

  In other words, when the identifier “0” is combined, the truth value rule application unit 102, for example, sets the truth value of the lower-level conditional expression common to the truth value combined with the identifier “0”. Combinations are combined and extracted using the selection operator “||”. Further, when the identifier “1” is combined, the truth value rule application unit 102 extracts all the combinations of the truth values of the conditional expressions common to the truth values combined with the identifier “1”, for example. To do.

  In the following description, in the logical product rule Tb1 illustrated in FIG. 7C, a record in which “1” is stored in the “No” column is also referred to as “record with ID = 1”. Similarly, records in which “2” and “3” are stored in the “No” column are also referred to as “records with ID = 2” and “records with ID = 3”.

For example, the truth value rule application unit 102 refers to the truth value rule DB 201 and acquires a truth value table corresponding to the node to be processed. In the example of FIG. 7C, leaf “x> 0”, “z == 5”
The node linking is the logical product “and”. For example, the truth value rule application unit 102 acquires the logical product rule Tb1 from the true value rule DB 201 and temporarily stores the acquired logical product rule Tb1 in a predetermined area of the main storage unit 12.

The truth value rule application unit 102 generates a conditional expression map set for each node based on a truth value table corresponding to the node, for example. The conditional expression map set for each node is generated from, for example, a conditional expression map generated for each record of the truth table. For example, the truth value rule application unit 102 is a conditional expression that reflects the truth value stored in the “whole” column of the truth value table and the truth value information of the “left side” column and the “right side” column. Combine with the join formula to generate a conditional formula map for each record. The generated conditional expression map set for each node generated based on the truth table corresponding to the node is temporarily stored in a predetermined area of the main storage unit 12, for example.

-Logical product rule 1
In the logical product rule Tb1 in FIG. 7C, truth value information (true, 0) is stored in the “left side” column of the record with ID = 1, and truth value information (false, 1) is stored in the “right side” column. Stored. The “left side” column corresponds to, for example, the leaf “x> 0” connected by the node, and the “right side” column corresponds to the leaf “z == 5” connected by the node. The combination rule 1 is applied to the true / false value of the leaf corresponding to the “left side” column, and the combination rule 2 is applied to the true / false value of the leaf corresponding to the “right side” column.

For example, the truth value rule application unit 102 sets the conditional expression map set of leaf “z == 5” based on the truth value information stored in the “right side” column of the record of ID = 1 of the logical product rule Tb1. From this, all conditional expressions having a true / false value “false” are extracted. Here, the conditional expression map set of the leaf “z == 5” is {((z == 5), true), (¬ (z == 5), false)}, and the truth value “false” The conditional expression combined with is a single “¬ (z == 5)”.

  The truth value rule application unit 102, for example, from the conditional expression map set of leaf “z == 5” as the conditional expression corresponding to the “right side” column of the record of ID = 1 of the logical product rule Tb1, the conditional expression “ ¬ (z == 5) "is extracted.

Next, the truth value rule application unit 102, for example, the conditional expression of leaf “x> 0” based on the truth value information stored in the “left side” column of the ID = 1 record of the logical product rule Tb1. From the map set, a conditional expression having a true / false value “true” is combined and extracted using a selection operator.

  Here, the conditional expression map set of leaf “x> 0” is {((x> 0), true), (¬ (x> 0), false)}, and is combined with the truth value “true”. The conditional expression is a single “(x> 0)”. Since there are not a plurality of conditional expressions for the true value “true” for the leaf “x> 0”, the selection operator is not used and the conditional expression “(x> combined with the true value“ true ”is not used. 0) "will be extracted.

For example, the truth value rule application unit 102 uses the conditional expression “(” from the conditional expression map set of leaf “x> 0” as the conditional expression corresponding to the “left side” column of the ID = 1 record of the logical product rule Tb1. x> 0) ”is extracted.

Then, the truth value rule application unit 102 generates a conditional expression map that is paired with the truth value “false” stored in the “whole” column for the record of ID = 1 of the logical product rule Tb1. The conditional expression map is, for example, a conditional expression (x> 0) extracted from the conditional expression map set of leaf “x> 0” and a conditional expression extracted from the conditional expression map set of leaf “z == 5”. It is generated based on (¬ (z == 5)).

The truth value rule application unit 102, for example, combines the conditional expression (x> 0) and the conditional expression (¬ (z == 5)) extracted from the conditional expression map set of each leaf with the symbol “ “((X> 0) ∧¬ (z == 5))” is generated by combining using “∧”. Then, the truth value rule application unit 102 combines the truth value “false” stored in the “whole” column of the ID = 1 record of the logical product rule Tb1, for example, and the truth value of the record ID = 1 A conditional expression map “((x> 0) ∧ ¬ (z == 5), false)” corresponding to is generated. The generated conditional expression map “((x> 0) ∧¬ (z == 5), false)” is associated with the ID of the logical product rule Tb1, for example, in a predetermined area of the main storage unit 12. Temporarily stored.

-Logical product rule 2
In the logical product rule Tb1 of FIG. 7C, the conditional expression map is generated for the record with ID = 2 in the same manner as with ID = 1. For example, truth value information (false, 1) is stored in the “left side” column of the record of ID = 2 of the logical product rule Tb1, and truth value information (true, 0) is stored in the “right side” column. Has been. The combination rule 2 is applied to the true / false value of the leaf corresponding to the “left side” column, and the combination rule 1 is applied to the true / false value of the leaf corresponding to the “right side” column.

The truth value rule application unit 102, for example, from the conditional expression map set of leaf “x> 0” based on the truth value information stored in the “left side” column of the record of ID = 2 of the logical product rule Tb1. , All the conditional expressions having the true / false value “false” are extracted. The conditional expression map set of leaf “x> 0” is {((x> 0), true), (¬ (x> 0), false)}, and the conditional expression combined with the truth value “false” Is a single “¬ (x> 0)”.

For example, the truth value rule application unit 102 uses the conditional expression “¬” from the conditional expression map set of leaf “x> 0” as the conditional expression corresponding to the “left side” column of the record of ID = 2 of the logical product rule Tb1. (x> 0) ”is extracted.

  Next, the truth value rule application unit 102, for example, the condition of the leaf “z == 5” based on the truth value information stored in the “right side” column of the record of ID = 2 of the logical product rule Tb1. From the expression map set, a conditional expression having a truth value “true” is combined and extracted using a selection operator.

Here, the conditional expression map set of the leaf “z == 5” is {((z == 5), true), (¬ (z == 5), false)}, and the truth value “true” The conditional expression combined with is a single “(z == 5)”. Since there are no multiple conditional expressions for the leaf “(z == 5)” that are true and false “true”, the selection operator is not used and the conditional expression “true” is combined with the true value “true”. (z == 5) ”is extracted.

  The truth value rule application unit 102, for example, from the conditional expression map set of leaf “z == 5” as the conditional expression corresponding to the “right side” column of the record of ID = 2 of the logical product rule Tb1, the conditional expression “ (z == 5) ”is extracted.

Then, the truth value rule application unit 102 generates a conditional expression map paired with the truth value “false” stored in the “whole” column of the record of ID = 2 of the logical product rule Tb1. The conditional expression map is extracted from, for example, a conditional expression (¬ (x> 0)) extracted from a conditional expression map set of leaf “x> 0” and a conditional expression map set of leaf “z == 5”. Is generated based on the conditional expression (z == 5).

The truth value rule application unit 102, for example, combines the conditional expression (¬ (x> 0)) and the conditional expression (z == 5) extracted from the conditional expression map set of each leaf with the symbol “ “(¬ (x> 0)) (z == 5))” is generated by combining using “∧”. Then, the truth value rule application unit 102 combines the truth value “false” stored in the “whole” column of the ID = 2 record of the logical product rule Tb1, for example, and the truth value of the record ID = 2 A conditional expression map “(¬ (x> 0) ∧ (z == 5), false)” corresponding to is generated. The generated conditional expression map “(¬ (x> 0) ∧ (z == 5), false)” is associated with the ID of the logical product rule Tb1, for example, in a predetermined area of the main storage unit 12. Temporarily stored.

-Logical product rule 3
The record of ID = 3 of the logical product rule Tb1 in FIG.
A conditional expression map is generated. For example, truth value information (true, 1) is stored in the “left side” column of the record of ID = 3 of the logical product rule Tb1, and truth value information (true, 1) is stored in the “right side” column. ing. Combination rule 2 is applied to the true / false values of the leaves corresponding to the “left side” column and the “right side” column.

The truth value rule application unit 102, for example, from the conditional expression map set of leaf “x> 0” based on the truth value information stored in the “left side” column of the ID = 3 record of the logical product rule Tb1. , All the conditional expressions for which the truth value is “true” are extracted. The conditional expression map set of leaf “x> 0” is {((x> 0), true), (¬ (x> 0), false)}, and the conditional expression combined with the truth value “true” Is a single “(x> 0)”.

For example, the truth value rule application unit 102 uses the conditional expression “(” from the conditional expression map set of leaf “x> 0” as the conditional expression corresponding to the “left side” column of the record of ID = 3 of the logical product rule Tb1. x> 0) ”is extracted.

Next, the truth value rule application unit 102, for example, the condition of leaf “z == 5” based on the truth value information stored in the “right side” column of the record of ID = 3 of the logical product rule Tb1. From the expression map set, all conditional expressions having a true / false value “true” are extracted. The conditional expression map set of leaf “z == 5” is {((z == 5), true), (¬ (z == 5), false)}, and is combined with the truth value “true”. The conditional expression is a single “(z == 5)”.

  The truth value rule application unit 102, for example, from the conditional expression map set of leaf “z == 5” as the conditional expression corresponding to the “right side” column of the record of ID = 3 of the logical product rule Tb1, the conditional expression “ (z == 5) ”is extracted.

  Then, the truth value rule application unit 102 generates a conditional expression map paired with the truth value “true” stored in the “whole” column of the record of ID = 3 of the logical product rule Tb1. The conditional expression map is, for example, a conditional expression (x> 0) extracted from the conditional expression map set of leaf “x> 0” and a conditional expression extracted from the conditional expression map set of leaf “z == 5”. Generated based on (z == 5).

For example, the truth value rule application unit 102 uses the conditional expression (x> 0) and the conditional expression (z == 5) extracted from the conditional expression map set of each leaf as the symbol “∧” representing the concatenation. Using the combination, “((x> 0) ∧ (z == 5))” is generated. Then, the truth value rule application unit 102 combines the truth value “true” stored in the “whole” column of the record with ID = 3 of the logical product rule Tb1, for example, and the truth value of the record with ID = 3. A conditional expression map “((x> 0) ∧ (z == 5), true)” corresponding to is generated. The generated conditional expression map “((x> 0) ∧ (z == 5), true)” is, for example, a logical product rule Tb
1 is temporarily stored in a predetermined area of the main storage unit 12 in association with one ID.

  Through the above processing, as shown in the block D2 of FIG. 7C, each conditional expression map based on the logical relationship of ID = 1-3 of the logical product rule Tb1 is generated.

Conditional Expression Map Set Based on the conditional expression map generated for each record for the logical product rule Tb1 in FIG. 7C, the truth value rule application unit 102 relates to the leaves “x> 0” and “z == 5”. A set of conditional expression maps reflecting the true / false values of the processing process is generated. For example, the truth value rule application unit 102 generates a conditional expression map set shown in the block D3 based on each conditional expression map shown in the block D2.

For example, the truth value rule application unit 102 combines the conditional expression maps generated for each record of the logical product rule Tb1, and sets the conditional expression map set {((x> 0)) ¬ (z == 5), false), (¬ (x> 0) ∧ (z
== 5), false), ((x> 0) ∧ (z == 5), true)}. A set of conditional expression maps reflecting the true / false values of the processing processes relating to the leaves “x> 0” and “z == 5” generated based on the conditional expression map generated for each record of the logical product rule Tb1 is For example, it is stored in the conditional expression map set DB 203.

(Second true / false rule application process)
Next, in the second truth value rule application process, the truth value rule application unit 102, for example, a conditional expression map for the node (logical operator) process arranged on the right side of the same hierarchy of the tree structure And a set of conditional expression maps. FIG. 7D illustrates an explanatory diagram of the generation process of the conditional expression map and the conditional expression map set for the tree structure of the determination expression C1 according to the second truth value rule application process.

In the tree structure illustrated in FIG. 7D, the node arranged on the right side of the same hierarchy as the node that is the processing target of the first true / false rule application processing is the logical product “and”, and the processing of the right node is performed. Each leaf related to the process is a leaf “y == 1” and “z> 1” shown in a rectangular area surrounded by a broken line.

First, the truth value rule application unit 102, for example, in the same way as the first truth value rule application process illustrated in FIG. 7B, leaves “y == 1” and “z” related to the processing process of the right node. Generates a conditional expression map and a conditional expression map set for> 1 ”.

  The truth value rule application unit 102 refers to, for example, the determination expression binary tree DB 202 and processes the nodes arranged on the left side of the lower layer from the tree structure (determination expression binary tree) generated for the determination expression to be processed. The leaf “y == 1” and “z> 1” related to the process is specified.

The truth value rule application unit 102 generates, for example, a conditional expression map ((y == 1), true) of the leaf “y == 1” in combination with the truth value of “true”. , A conditional expression map (¬ (y == 1), false) in combination with a false value of “false” is generated.
02, for example, combines the conditional expression maps generated for the leaf “y == 1”, and sets the conditional expression map set {((y == 1), true), (¬ (y == 1) shown in the block D4 , False)}.

Further, the truth value rule application unit 102, for example, for the leaf “z> 1”, “true”
Generates a conditional expression map ((z> 1), true) in combination with a true / false value of “” and creates a conditional expression map (¬ (z> 1), false in combination with a false / false value) ) Is generated. For example, the truth value rule application unit 102 combines the conditional expression maps generated for the leaf “z> 1”, and sets the conditional expression map set {((z> 1), true), (¬ (z > 1), false)}.

The truth value rule application unit 102 temporarily stores the conditional expression map set generated for each of the leaves “y == 1” and “z> 1”, for example, in a predetermined area of the main storage unit 12. .

Next, the true / false value rule application unit 102 calculates a true / false value for the processing process of the node linking the leaves “y == 1” and “z> 1” based on the conditional expression map set generated for each leaf. Generate the reflected conditional expression map set.

FIG. 7E illustrates an explanatory diagram of generation processing of a conditional expression map set reflecting a true / false value for a processing process related to leaves “y == 1” and “z> 1”. In the explanatory diagram of FIG. 7E, the conditional expression map set generated for the leaf “y == 1” is also referred to as the “conditional map set on the left side” and the leaf “z> 1” as shown in the block D4. The conditional expression map set generated for is also referred to as a “right-side conditional expression map set”.

  In the explanatory diagram of FIG. 7E, the logical product rule Tb1 represents a truth table of logical products to which the combination rule is applied. The logical product rule Tb1 has been described with reference to FIG. 7C. In the following description, in the logical product rule Tb1 illustrated in FIG. 7E, a record in which “1” is stored in the “No” column is also referred to as “record with ID = 1”. Similarly, records in which “2” and “3” are stored in the “No” column are also referred to as “records with ID = 2” and “records with ID = 3”.

For example, the truth value rule application unit 102 refers to the truth value rule DB 201 and acquires a truth value table corresponding to the node to be processed. In the example of FIG. 7E, the leaf “y == 1”, “z> 1”
The node linking is the logical product “and”. For example, the truth value rule application unit 102 acquires the logical product rule Tb1 from the true value rule DB 201 and temporarily stores the acquired logical product rule Tb1 in a predetermined area of the main storage unit 12.

  The truth value rule application unit 102 generates a conditional expression map set for each node based on a truth value table corresponding to the node, for example. The conditional expression map set for each node is generated from, for example, a conditional expression map generated for each record of the truth table. For example, the truth value rule application unit 102 is a conditional expression that reflects the truth value stored in the “whole” column of the truth value table and the truth value information of the “left side” column and the “right side” column. Combine with the join formula to generate a conditional formula map for each record. The conditional expression map set for each node generated based on the truth table corresponding to the node is temporarily stored in a predetermined area of the main storage unit 12, for example.

The generation process of the conditional expression map set for each node in the second truth value rule application process is the same as the generation process of the conditional expression map set for each node in the first truth value rule application process, for example. Processing is performed. That is, a conditional expression map for each record is generated along the logical product table Tb1, and the generated conditional expression map for each record is combined to generate a conditional expression map set for each node.

-Logical product rule 1
In the logical product rule Tb1 of FIG. 7E, truth value information (true, 0) is stored in the “left side” column of the record of ID = 1, and truth value information (false, 1) is stored in the “right side” column. Stored. Therefore, the combination rule 1 is applied to the truth value of the leaf “y == 1” corresponding to the “left side” column, and the combination rule is applied to the truth value of the leaf “z> 1” corresponding to the “right side” column. Rule 2
Will be applied.

The truth value rule application unit 102, for example, from the conditional expression map set of leaf “z> 1” based on the truth value information stored in the “right side” column of the record of ID = 1 of the logical product rule Tb1. , All the conditional expressions having the true / false value “false” are extracted. Here, the conditional expression map set of leaf “z> 1” is {((z> 1), true), (¬ (z> 1), false)}, and is combined with the truth value “false”. The conditional expression is a single “¬ (z> 1)”.

The truth value rule application unit 102, for example, from the conditional expression map set of leaf “z> 1” as the conditional expression corresponding to the “right side” column of the record of ID = 1 of the logical product rule Tb1, the conditional expression “¬ (z> 1) ”is extracted.

Next, the truth value rule application unit 102, for example, the condition of leaf “y == 1” based on the truth value information stored in the “left side” column of the ID = 1 record of the logical product rule Tb1. From the expression map set, a conditional expression having a truth value “true” is combined and extracted using a selection operator. Here, the conditional expression map set of leaf “y == 1” is {((y == 1), true), (¬ (y == 1), false)}
The conditional expression combined with the true / false value “true” does not exist in plural and is a single “(y == 1)”. For this reason, the conditional expression “(y == combined with the truth value“ true ”is used without using the selection operator.
1) ”will be extracted.

  The truth value rule application unit 102, for example, from the conditional expression map set of leaf “y == 1” as the conditional expression corresponding to the “left side” column of the record of ID = 1 of the logical product rule Tb1, the conditional expression “ (y == 1) ”is extracted.

Then, the truth value rule application unit 102 generates a conditional expression map that is paired with the truth value “false” stored in the “whole” column for the record of ID = 1 of the logical product rule Tb1. The conditional expression map is, for example, a conditional expression (y == 1) extracted from the conditional expression map set of leaf “y == 1” and a conditional expression extracted from the conditional expression map set of leaf “z> 1”. It is generated based on the formula (¬ (z> 1)).

The truth value rule application unit 102, for example, combines the conditional expression (y == 1) and the conditional expression (¬ (z> 1)) extracted from the conditional expression map set of each leaf with the symbol “ "((Y == 1)
¬¬ (z> 1)) ”and the truth value rule application unit 102, for example, the truth value“ false ”stored in the“ whole ”column of the record of ID = 1 of the logical product rule Tb1. ”And a conditional expression map“ ((y == 1) ∧¬ (z> 1), false) ”corresponding to the truth value of the record with ID = 1 is generated. The generated conditional expression map “((y == 1) ∧¬ (z> 1), false)” is associated with the ID of the logical product rule Tb1, for example, in a predetermined area of the main storage unit 12. Temporarily stored.

-Logical product rule 2
In the logical product rule Tb1 of FIG. 7E, truth value information (false, 1) is stored in the “left side” column of the record with ID = 2, and truth value information (true, 0) is stored in the “right side” column. Stored. Therefore, the combination rule 2 is applied to the true value of the leaf “y == 1” corresponding to the “left side” column, and the combination rule is applied to the true value of the leaf “z> 1” corresponding to the “right side” column. Rule 1
Will be applied.

For example, the truth value rule application unit 102 sets the conditional expression map set of the leaf “y == 1” based on the truth value information stored in the “left side” column of the record of ID = 2 of the logical product rule Tb1. From this, all conditional expressions having a true / false value “false” are extracted. The conditional expression map set of leaf “y == 1” is {((y == 1), true), (¬ (y == 1), false)}, and is combined with the truth value “false”. The conditional expression is a single “¬ (y == 1)”.

  The truth value rule application unit 102, for example, from the conditional expression map set of the leaf “y == 1” as the conditional expression corresponding to the “left side” column of the record of ID = 2 of the logical product rule Tb1, the conditional expression “ ¬ (y == 1) "is extracted.

Next, the truth value rule application unit 102, for example, the conditional expression of the leaf “z> 1” based on the truth value information stored in the “right side” column of the record of ID = 2 of the logical product rule Tb1. From the map set, a conditional expression having a true / false value “true” is combined and extracted using a selection operator. The conditional expression map set of leaf “z> 1” is {((z> 1), true), (¬ (z> 1), false)}, and the conditional expression combined with the truth value “true” Is a single “(z> 1)”, without multiple occurrences. Therefore, the conditional expression “(z> 1)” combined with the truth value “true” is extracted without using the selection operator.

For example, the truth value rule application unit 102 uses the conditional expression “(” from the conditional expression map set of leaf “z> 1” as the conditional expression corresponding to the “right side” column of the record of ID = 2 of the logical product rule Tb1. z> 1) ”is extracted.

Then, the truth value rule application unit 102 generates a conditional expression map paired with the truth value “false” stored in the “whole” column for the record of ID = 2 of the logical product rule Tb1. The conditional expression map is extracted from, for example, the conditional expression (¬ (y == 1)) extracted from the conditional expression map set of leaf “y == 1” and the conditional expression map set of leaf “z> 1” Is generated based on the conditional expression (z> 1).

The truth value rule application unit 102, for example, combines the conditional expression (¬ (y == 1)) and the conditional expression (z> 1) extracted from the conditional expression map set of each leaf with the symbol “ “(¬ (y == 1) ∧ (z> 1))” is generated by combining using “∧”. Then, the truth value rule application unit 102 combines the truth value “false” stored in the “whole” column of the ID = 2 record of the logical product rule Tb1, for example, and the truth value of the record ID = 2 A conditional expression map “(¬ (y == 1) ∧ (z> 1), false)” corresponding to is generated. The generated conditional expression map “(¬ (y == 1) ∧ (z> 1), false)” is associated with the ID of the logical product rule Tb1, for example, in a predetermined area of the main storage unit 12. Temporarily stored.

-Logical product rule 3
In the logical product rule Tb1 of FIG. 7E, truth value information (true, 1) is stored in the “left side” column of the record with ID = 3, and truth value information (true, 1) is stored in the “right side” column. Stored. Therefore, the combination rule 2 is applied to the true / false value of the leaf “y == 1” corresponding to the “left side” column and the true / false value of the leaf “z> 1” corresponding to the “right side” column. It will be.

The truth value rule application unit 102, for example, sets the conditional expression map set of leaf “y == 1” based on the truth value information stored in the “left side” column of the ID = 3 record of the logical product rule Tb1. From this, all conditional expressions having a true / false value “true” are extracted. The conditional expression map set of leaf “y == 1” is {((y == 1), true), (¬ (y == 1), false)}, and is combined with the truth value “true”. The conditional expression is a single “(y == 1)”.

  The truth value rule application unit 102, for example, from the conditional expression map set of leaf “y == 1” as the conditional expression corresponding to the “left side” column of the record of ID = 3 of the logical product rule Tb1, the conditional expression “ (y == 1) ”is extracted.

Next, the truth value rule application unit 102, for example, the conditional expression of the leaf “z> 1” based on the truth value information stored in the “right side” column of the record of ID = 3 of the logical product rule Tb1. From the map set, all conditional expressions with a true / false value “true” are extracted. The conditional expression map set of leaf “z> 1” is {((z> 1), true), (¬ (z> 1), false)}, and the conditional expression combined with the truth value “true” Is a single “(z> 1)”.

The truth value rule application unit 102, for example, from the conditional expression map set of leaf “z> 1” as the conditional expression corresponding to the “right side” column of the record of ID = 3 of the logical product rule Tb1, the conditional expression “( z> 1) ”is extracted.

  Then, the true / false value rule application unit 102 generates a conditional expression map that is paired with the true / false value “true” stored in the “whole” column for the record of ID = 3 of the logical product rule Tb1. The conditional expression map is, for example, a conditional expression (y == 1) extracted from the conditional expression map set of leaf “y == 1” and a conditional expression extracted from the conditional expression map set of leaf “z> 1”. It is generated based on the formula (z> 1).

The truth value rule application unit 102, for example, uses the conditional expression (y == 1) and the conditional expression (z> 1) extracted from the conditional expression map set of each leaf as the symbol “∧” representing the concatenation. To create “((y == 1) ∧ (z> 1))”. Then, the truth value rule application unit 102 performs, for example, the AND rule Tb
Combined with the true / false value “true” stored in the “whole” column of the ID = 3 record of 1 and the conditional expression map “((y == 1) ∧ ( z> 1), true) ”. The generated conditional expression map “((y == 1) ∧ (z> 1), true)” is, for example, a logical product rule Tb
1 is temporarily stored in a predetermined area of the main storage unit 12 in association with one ID.

  Through the above processing, as shown in block D5 in FIG. 7E, each conditional expression map based on the logical relationship of ID = 1-3 of the logical product rule Tb1 is generated.

Conditional Expression Map Set Based on the conditional expression map generated for each record of the logical product rule Tb1 in FIG. 7E, the truth value rule applying unit 102 relates to the leaves “y == 1” and “z> 1”. A set of conditional expression maps reflecting the true / false values of the processing process is generated. For example, the truth value rule application unit 102 generates a conditional expression map set shown in the block D6 based on each conditional expression map shown in the block D5.

For example, the truth value rule application unit 102 combines the conditional expression maps generated for each record of the logical product rule Tb1, and sets the conditional expression map set {(((y == 1)) ¬ (z> 1), false), (¬ (y == 1) ∧ (z> 1), false), ((y == 1) ∧ (z> 1), true)}. A set of conditional expression maps reflecting the true / false values for the processing processes related to the leaves “y == 1” and “z> 1” generated based on the conditional expression map generated for each record of the logical product rule Tb1 is For example, it is stored in the conditional expression map set DB 203.

(Third Boolean value rule application process)
Next, in the third true / false value rule application process, the true / false value rule application unit 102 uses, for example, a conditional expression for a node (logical operator) process that links each node of a lower processing process in a tree structure. A map and a conditional expression map set are generated.

FIG. 7F illustrates an explanatory diagram of the generation process of the conditional expression map and the conditional expression map set for the tree structure of the determination expression C1 according to the third truth value rule application process. In the tree structure illustrated in FIG. 7F, as shown in a rectangular area surrounded by a broken line, an upper node that connects lower nodes (logical product “and”) arranged in the same hierarchy is a logical “or”. In the third true / false value rule application process, for example, an upper node (logical “or”) process for linking each lower node based on a conditional expression map set generated for each lower node arranged in the same hierarchy. A conditional expression map and a set of conditional expression maps are generated.

In the tree structure illustrated in FIG. 7F, the conditional expression map set generated at the left lower node connecting the leaves “x> 0” and “z == 5” is {((x> 0) ∧ ¬ (z = = 5), false), (¬ (x> 0) ∧ (z == 5), false), ((x> 0) ∧ (z == 5), true)}. In addition, the conditional expression map set generated at the right lower node connecting the leaves “y == 1” and “z> 1” is {((y == 1) ∧ ¬ (z> 1), false), (¬ (y == 1) ∧ (z> 1), false), ((y == 1) ∧ (z> 1), true)}.

In the explanatory diagram of FIG. 7F, the leaf “x> 0”, “z == 5” as shown in the block D7.
The conditional expression map set generated at the left subordinate node that connects "" is also referred to as the "left-side conditional expression map set". Similarly, as shown in block D7, the leaves "y == 1", "z> 1
The conditional expression map set generated at the right-side lower node that ties "is also referred to as the" right-side conditional expression map set ".

In the explanatory diagram of FIG. 7F, a logical sum rule Tb2 represents a truth table of logical sums to which the combination rule is applied. The truth value information stored in the “left side” column and the “right side” column of the logical sum rule Tb2 has been described with reference to FIG. 7C. In the following description, a record in which “1” is stored in the “No” column in the logical sum rule Tb2 illustrated in FIG. 7F is also referred to as “record of ID = 1”. Similarly, records in which “2” and “3” are stored in the “No” column are also referred to as “records with ID = 2” and “records with ID = 3”.

For example, the truth value rule application unit 102 refers to the truth value rule DB 201 and acquires a truth value table corresponding to the node to be processed. In the tree structure illustrated in FIG. 7F, an upper node that connects lower nodes (logical product “and”) arranged in the same hierarchy is a logical sum “or”. For example, the truth value rule application unit 102 acquires the logical sum rule Tb2 from the truth value rule DB 201, and temporarily stores the acquired logical sum rule Tb2 in a predetermined area of the main storage unit 12.

  For example, the truth value rule application unit 102 is a conditional expression that reflects the truth value stored in the “whole” column of the truth value table and the truth value information of the “left side” column and the “right side” column. Combine with the join formula to generate a conditional formula map for each record. Then, the truth value rule application unit 102 combines, for example, a conditional expression map for each record from a conditional expression map generated for each record of the truth table, and a conditional expression map for the processing process of the node to be processed. Create a set. The set of conditional expression maps generated for the processing process of the node to be processed is temporarily stored in a predetermined area of the main storage unit 12, for example.

・ OR rule 1
In the logical sum rule Tb2 of FIG. 7F, truth value information (true, 1) is stored in the “left side” column of the record of ID = 1, and truth value information (false, 0) is stored in the “right side” column. Stored. Here, the “left side” column corresponds to, for example, the conditional expression map set of the left lower node that is linked by the upper node that is the processing target, and the “left side” column is the right side that is linked by the upper node that is the processing target. Corresponds to the set of conditional expression maps of lower nodes.

  Therefore, the combination rule 2 is applied to the truth value of the conditional expression map set of the left lower node corresponding to the “left side” column, and the truth of the conditional expression map set of the right lower node corresponding to the “right side” column is applied. Combination rule 1 is applied to the false value.

The truth value rule application unit 102, for example, from the conditional expression map set of the left lower node based on the truth value information stored in the “left side” column of the ID = 1 record of the logical sum rule Tb2. Extract all conditional expressions that have the false value “true”. Here, the conditional expression map set of the lower left node is {((x> 0) ¬ ¬ (z == 5), false), (¬ (x> 0) ∧ (z == 5), false) , ((X> 0)
∧ (z == 5), true)}, and the conditional expression combined with the truth value “true” is a single “(x> 0) ∧ (z == 5)”.

  For example, the truth value rule application unit 102 uses the conditional expression “(x>) from the conditional expression map set of the left lower node as a conditional expression corresponding to the“ left side ”column of the ID = 1 record of the logical sum rule Tb2. 0) ∧ (z == 5) ”is extracted.

  Next, the truth value rule application unit 102, for example, sets the conditional expression map set of the lower right node based on the truth value information stored in the “right side” column of the record of ID = 1 of the logical sum rule Tb2. From the above, the conditional expressions for which the truth value is “false” are combined and extracted using a selection operator.

Here, the set of conditional expression maps generated at the lower right node is {((y == 1) ∧ ¬ (z> 1), false), (¬ (y == 1) ∧ (z> 1) , False), ((y == 1) ∧ (z> 1), true)}. There are multiple conditional expressions that are combined with the truth value “false”, and they are “(y == 1) ∧ ¬ (z> 1)”, “¬ (y == 1) ∧ (z> 1)” There are two ways. Therefore, in the truth value rule application unit 102, for example, a plurality of conditional expressions combined with a true value “false” are combined using the selection operator “||”, and the combined conditional expression “(( y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z> 1)) ”is extracted.

  The truth value rule application unit 102 uses, for example, a selection operator from the conditional expression map set of the right lower node as a conditional expression corresponding to the “right side” column of the record of ID = 1 of the logical sum rule Tb2. The combined conditional expression “((y == 1) ∧¬ (z> 1) || ¬ (y == 1) = 1 (z> 1))” is extracted.

Then, the true / false value rule application unit 102 generates a conditional expression map paired with the true / false value “true” stored in the “whole” column for the record of ID = 1 of the logical sum rule Tb2. The conditional expression map is extracted from, for example, the conditional expression “(x> 0) ∧ (z == 5)” extracted from the conditional expression map set of the left lower node and the conditional expression map set of the right lower node. Is generated based on the conditional expression “((y == 1) ∧¬ (z> 1) || ¬ (y == 1) ∧ (z> 1))”.

For example, the truth value rule application unit 102 combines “(x> 0) ∧ (z) by combining the conditional expressions extracted from the conditional expression map set of each lower node using the symbol“ ∧ ”representing the concatenation. == 5) ∧ ((y == 1) ∧
¬ (z> 1) || ¬ (y == 1) ∧ (z> 1)) ”.

  Then, the truth value rule application unit 102 combines the truth value “true” stored in the “whole” column of the record with ID = 1 of the logical sum rule Tb2, for example, and the truth value of the record with ID = 1. A conditional expression map corresponding to is generated. In the record of ID = 1 of the logical sum rule Tb2, as shown in the block D8, the conditional expression map “((x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1 ) || ¬ (y == 1) ∧ (z> 1)), true) ”is generated.

  Generated conditional expression map “((x> 0) ∧ (z == 5)) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z> 1 )), True) ”are temporarily stored in a predetermined area of the main storage unit 12 in association with the ID of the logical sum rule Tb2, for example.

・ OR rule 2
In the logical sum rule Tb2 of FIG. 7F, truth value information (false, 0) is stored in the “left side” column of the record of ID = 2, and truth value information (true, 1) is stored in the “right side” column. Stored. Therefore, the combination rule 1 is applied to the truth value of the conditional expression map set of the left lower node corresponding to the “left side” column, and the truth of the conditional expression map set of the right lower node corresponding to the “right side” column is applied. The combination rule 2 is applied to the false value.

  The truth value rule application unit 102, for example, from the conditional expression map set of the lower left node based on the truth value information stored in the “left side” column of the record of ID = 2 of the logical sum rule Tb2. A conditional expression with a false value “false” is combined and extracted using a selection operator.

Here, the conditional expression map set of the lower left node is {((x> 0) ∧ ¬ (z == 5), false), (
¬ (x> 0) ∧ (z == 5), false), ((x> 0) ∧ (z == 5), true)}. There are multiple conditional expressions that are combined with the truth value “false”, and “(x> 0) ¬ ¬ (z == 5)”, “¬ (x> 0) ∧ (z == 5)” There are two ways. Therefore, in the truth value rule application unit 102, for example, a plurality of conditional expressions combined with a true value “false” are combined using the selection operator “||”, and the combined conditional expression “(( x> 0)
¬ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ”is extracted.

  The truth value rule application unit 102 uses, for example, a selection operator from the conditional expression map set of the left lower node as the conditional expression corresponding to the “left side” column of the record of ID = 2 of the logical sum rule Tb2. The combined conditional expression “((x> 0) ∧¬ (z == 5) || ¬ (x> 0) ∧ (z == 5))” is extracted.

  Next, the truth value rule application unit 102, for example, sets the conditional expression map set of the lower right node based on the truth value information stored in the “right side” column of the record of ID = 1 of the logical sum rule Tb2. From this, all conditional expressions having a true / false value “true” are extracted. Here, the set of conditional expression maps generated at the lower right node is {((y == 1) ∧ ¬ (z> 1), false), (¬ (y == 1) ∧ (z> 1) , False), ((y == 1) ∧ (z> 1), true)}, and the conditional expression combined with the true value “true” is a single “(y == 1) ∧ ( z> 1) ”.

  The truth value rule application unit 102, for example, from the conditional expression map set of the right lower node as the conditional expression corresponding to the “right side” column of the record of ID = 2 of the logical sum rule Tb2, the conditional expression “(y = = 1) Extract ∧ (z> 1) ”.

  Then, the truth value rule application unit 102 generates a conditional expression map that is paired with the truth value “true” stored in the “whole” column for the record of ID = 2 of the logical sum rule Tb2. The conditional expression map is, for example, a conditional expression “((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ”and the conditional expression“ (y == 1) ∧ (z> 1) ”extracted from the conditional expression map set of the lower right node.

  The truth value rule application unit 102 combines, for example, the conditional expressions extracted from the conditional expression map set of each lower node by using the symbol “∧” representing the concatenation, “((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ∧ (y == 1) ∧ (z> 1) ”.

  Then, the truth value rule application unit 102 combines the truth value “true” stored in the “whole” column of the record of ID = 2 of the logical sum rule Tb2, for example, and the truth value of the record of ID = 2 A conditional expression map corresponding to is generated. In the record of ID = 2 of the logical sum rule Tb2, as shown in the block D8, the conditional expression map “(((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z = = 5)) ∧ (y == 1) ∧ (z> 1), true) ”is generated.

  Generated conditional expression map “((((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ∧ (y == 1) ∧ (z> 1 ), True) ”is temporarily stored in a predetermined area of the main storage unit 12 in association with the ID of the logical sum rule Tb2, for example.

・ OR rule 3
In the logical sum rule Tb2 of FIG. 7F, truth value information (false, 1) is stored in the “left side” column of the record with ID = 3, and truth value information (false, 1) is stored in the “right side” column. Stored. For this reason, the combination rule is applied to the truth value of the conditional expression map set of the left lower node corresponding to the “left side” column and the truth value of the conditional expression map set of the right lower node corresponding to the “right side” column. 2 will be applied.

  The truth value rule application unit 102, for example, from the conditional expression map set of the left lower node based on the truth value information stored in the “left side” column of the record of ID = 3 of the logical sum rule Tb2. Extract all conditional expressions that have a false value “false”.

Here, the conditional expression map set of the lower left node is {((x> 0) ∧ ¬ (z == 5), false), (
¬ (x> 0) ∧ (z == 5), false), ((x> 0) ∧ (z == 5), true)}. There are two conditional expressions combined with the truth value “false”: {(x> 0) 0¬ (z == 5), ¬ (x> 0) ∧ (z == 5)}. For this reason, in the truth value rule application unit 102, for example, all the two conditional expressions combined with the truth value “false” are extracted. Note that the two conditional expressions combined with the true / false value “false” are extracted, for example, in a set format using the symbol “{}”.

The truth value rule application unit 102, for example, ““ of the record of ID = 3 of the logical sum rule Tb2 ”
As a conditional expression corresponding to the “left-hand side” column, “{(x> 0) ∧ ¬ (z == 5), ¬ (x> 0) ∧ (z == 5)} ”is extracted.

  Next, the truth value rule application unit 102, for example, sets the conditional expression map set of the lower-level node on the right based on the truth value information stored in the “right side” column of the record of ID = 3 of the logical sum rule Tb2. From this, all conditional expressions having a true / false value “false” are extracted.

Here, the set of conditional expression maps generated at the lower right node is {((y == 1) ∧ ¬ (z> 1), false), (¬ (y == 1) ∧ (z> 1) , False), ((y == 1) ∧ (z> 1), true)}. There are two conditional expressions combined with the truth value “false”: {(y == 1) = 1¬ (z> 1), ¬ (y == 1) ∧ (z> 1)}. For this reason, the true / false value rule applying unit 102 extracts all the two conditional expressions combined with the true / false value “false” even in the lower-level node on the right side, for example. The two conditional expressions combined with the true / false value “false” are extracted in a set form using the symbol “{}”, for example.

  The truth value rule application unit 102, for example, from the conditional expression map set of the lower right node as a conditional expression corresponding to the “right side” column of the ID = 3 record of the logical sum rule Tb2 in the form of a conditional expression set. “{(Y == 1) ∧¬ (z> 1), ¬ (y == 1) ∧ (z> 1)}” is extracted.

Then, the truth value rule application unit 102 combines the truth value “false” stored in the “whole” column of the record with ID = 3 of the logical sum rule Tb2, for example, and the truth value of the record with ID = 3. A conditional expression map corresponding to is generated. Note that, with ID = 3 of the logical sum rule Tb2, two conditional expressions are extracted for the left lower node, and two conditional expressions are extracted for the right lower node. For this reason, with ID = 3 of the logical sum rule Tb2, conditional expression 2 × conditional expression 2 = 4 combinations of conditional expressions that are combinations of conditional expressions extracted at each lower node are generated. The truth value rule application unit 102, for example, for each of the four conditional expressions generated from the combination of conditional expressions extracted at each lower node, the “whole” column of the record of ID = 3 of the logical sum rule Tb2 The conditional expression map is generated by combining the truth values “false” stored in.

The truth value rule application unit 102 represents, for example, “{(x> 0) ∧ ¬ (z == 5), ¬ (x> 0) ∧ (z == 5)} expressed in a set of conditional expressions. ”And“ {(y == 1) ∧¬ (z> 1), ¬ (y == 1) ∧ (z> 1)} ”are respectively combined to generate four conditional expressions. The generated conditional expressions are “((x> 0) ∧ ¬ (z == 5)) ∧ ((y == 1) ∧ ¬ (z> 1))”, “((x> 0) ∧ ¬ (z == 5)) ∧ (¬ (y == 1) ∧ (z> 1)) ”,“ (¬ (x> 0) ∧ (z == 5)) ∧ ((y == 1) ∧ ¬ (z> 1)) ”,“ (¬ (x> 0) ∧ (z == 5)) ∧ (¬ (y == 1)
∧ (z> 1)) ”.

Then, the true / false value rule application unit 102 generates a conditional expression map in which each of the four conditional expressions is combined with the true / false value “false”, as shown in block D8, for example. The generated conditional expression map is expressed as follows, for example, in a set form using the symbol “{}”.

Conditional expression map: {((x> 0) ∧ ¬ (z == 5) ∧ (y == 1) ∧ ¬ (z> 1), false), ((x> 0) ∧ ¬ (z == 5 ) ∧ ¬ (y == 1) ∧ (z> 1), false), (¬ (x> 0) ∧ (z == 5) ∧ (y == 1) ∧ ¬ (z> 1), false) , (¬ (x> 0) ∧ (z == 5) ∧ ¬ (y == 1) ∧ (z> 1), false)}
For example, the truth value rule application unit 102 associates the conditional expression map generated by reflecting the truth value of the record of ID = 3 of the logical sum rule Tb2 with, for example, the ID of the logical sum rule Tb2, The data is temporarily stored in a predetermined area of the main storage unit 12.

Conditional expression map set Based on the conditional expression map generated for each record of the logical sum rule Tb2 in FIG. 7F, the truth value rule application unit 102 sets the upper node that connects a plurality of lower nodes arranged in the same hierarchy. A set of conditional expression maps reflecting the truth value for the processing process is generated. For example, the truth value rule application unit 102 generates a conditional expression map set shown in the block D9 based on each conditional expression map shown in the block D8.

  For example, the truth value rule application unit 102 combines the conditional expression maps generated for each record of the logical sum rule Tb2, and generates a conditional expression map set shown below. The generated conditional expression map set is stored in the conditional expression map set DB 203, for example.

Conditional expression map set: {((x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z> 1))
, True), (((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ∧ (y == 1) ∧ (z> 1), true ), ((X> 0) ∧ ¬ (z == 5) ∧ (y == 1) ∧ ¬ (z> 1), false), ((x> 0) ∧ ¬ (z == 5) ∧ ¬ (y == 1) ∧ (z> 1), false), (¬ (x> 0) ∧ (z == 5) ∧ (y == 1) ∧ ¬ (z> 1), false), (¬ (x> 0) ∧ (z == 5) ∧ ¬ (y == 1) ∧ (z> 1), false)}

(Fourth Truth Value Rule Application Process)
In the fourth truth value rule application process, the truth value rule application unit 102 uses, for example, a conditional expression map and a conditional expression map set for a node (logical operator) process of the highest processing process in a tree structure. Generate. The truth value rule application processing unit 102, for example, the highest node (logical product “and”) that connects the node that is the processing target of the third truth value rule application processing and the leaf arranged in the same hierarchy. A conditional expression map and a conditional expression map set for the processing process are generated.

FIG. 7G illustrates an explanatory diagram of the generation process of the conditional expression map and the conditional expression map set for the tree structure of the determination expression C1 according to the fourth truth value rule application process. In the tree structure illustrated in FIG. 7G, the leaf arranged in the same hierarchy as the node that is the processing target of the third truth value rule application process is the leaf “y” as shown in the rectangular area surrounded by the broken line. > 0 ”. The truth value rule application unit 102 refers to, for example, the determination expression binary tree DB 202, identifies the leaf “y> 0” from the tree structure illustrated in FIG. 7G, and the conditional expression map for the leaf “y> 0” A conditional expression map set is generated.

For example, for the leaf “y> 0”, the truth value rule application unit 102 generates a conditional expression map ((y> 0), true) in combination with the truth value of “true”. A conditional expression map (¬ (y> 0), false) in combination with a false value is generated, and the truth value rule application unit 102, for example, leaves “(y> 0)”. Combining the conditional expression maps generated for the block D10
A conditional expression map set {((y> 0), true), (¬ (y> 0), false)} is generated. The truth value rule application unit 102, for example, sets of conditional expression maps {{>
((Y> 0), true), (¬ (y> 0), false)} are temporarily stored in a predetermined area of the main storage unit 12.

The truth value rule application unit 102, for example, based on the conditional expression map set generated by the third truth value rule application process and the conditional expression map set generated by the leaf “y> 0”, A set of conditional expression maps reflecting the truth value about the processing process of the upper node is generated.

FIG. 7H illustrates an explanatory diagram of a conditional expression map set reflecting a true / false value related to the processing process for the highest node. In the explanatory diagram of FIG. 7H, the conditional expression map set generated by the third true / false value rule application process is also referred to as a “left-side conditional expression map set” as indicated by a block D9. The set of conditional expression maps generated with leaf “y> 0” is the block D
As shown in FIG. 10, it is also referred to as a “right-side conditional expression map set”.

  Here, the conditional expression map set generated with leaf “y> 0” is {((y> 0), true), (¬ (y> 0), false)}. The conditional expression map set generated by the third truth value rule application process is as follows.

Conditional expression map set generated by the third truth value rule application process: {((x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z> 1)), true), (((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ∧ ( y == 1) ∧ (z> 1), true), ((x> 0) ∧ ¬ (z == 5) ∧ (y == 1) ∧ ¬ (z> 1), false)
, ((X> 0) ∧ ¬ (z == 5) ∧ ¬ (y == 1) ∧ (z> 1), false), (¬ (x> 0) ∧ (z == 5) ∧ (y == 1)
∧ ¬ (z> 1), false), (¬ (x> 0) ∧ (z == 5) ∧ ¬ (y == 1) ∧ (z> 1), false)}
In the explanatory diagram of FIG. 7H, the logical product rule Tb1 represents a truth table of the logical product to which the combination rule is applied as illustrated in FIG. 7C and the like. In the following description, a record in which “1” is stored in the “No” column in the logical product rule Tb1 illustrated in FIG. 7F is also referred to as “record of ID = 1”. Similarly, records in which “2” and “3” are stored in the “No” column are also referred to as “records with ID = 2” and “records with ID = 3”.

  For example, the truth value rule application unit 102 refers to the truth value rule DB 201 and acquires a truth value table corresponding to the node to be processed. In the example of FIG. 7H, as shown in the rectangular area surrounded by the broken line, the left node (logical sum “or”) arranged in the same hierarchy and the leaf “y> 0” arranged on the right side are connected. The upper node is the logical product “and”. For example, the truth value rule application unit 102 acquires the logical product rule Tb1 from the true value rule DB 201 and temporarily stores the acquired logical product rule Tb1 in a predetermined area of the main storage unit 12.

  The truth value rule application unit 102 generates a conditional expression map set for the processing process of the highest node based on, for example, a truth table corresponding to the highest node. The conditional expression map set for the processing process of the highest node is generated from a combination of conditional expression maps generated for each record, for example. The Boolean value rule application unit 102 reflects, for example, the Boolean value stored in the “whole” column of the Boolean value table and the Boolean value information in the “left side” column and the “right side” column for each record. A combination of conditional expressions is combined to generate a conditional expression map for each record. The conditional expression map set generated for the processing process of the highest node is stored in the conditional expression map set DB 203, for example.

-Logical product rule 1
In the logical product rule Tb1 of FIG. 7H, truth value information (true, 0) is stored in the “left side” column of the record of ID = 1, and truth value information (false, 1) is stored in the “right side” column. Stored. Therefore, the combination rule 1 is applied to the true / false value of the conditional expression map set of the left node (logical sum “or”) corresponding to the “left side” column, and the right leaf “y” corresponding to the “right side” column. Combination rule 2 is applied to a true / false value of> 0 ”.

  For example, the truth value rule application unit 102 determines whether the truth value is true from the conditional expression map set of the left node based on the truth value information stored in the “left side” column of the ID = 1 record of the logical product rule Tb1. A conditional expression having the value “true” is extracted by using a selection operator.

  In the conditional expression map set of the left node, the conditional expression combined with the truth value “true” is “(x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z> 1)) ”,“ ((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5) ) ∧ (y == 1) ∧ (z> 1) ”. The truth value rule application unit 102 uses, for example, a selection operator from the conditional expression map set of the left node as a conditional expression corresponding to the “left side” column of the ID = 1 record of the logical product rule Tb1. Then, the following conditional expressions are extracted. The following conditional expressions represent two types of conditional expressions that are selected using the symbol “[]” to identify the hierarchical relationship of selection operators.

Conditional expression on the left side of ID = 1: ([(x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z > 1))] || [((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ∧ (y == 1) ∧ (z> 1 )])
Next, the truth value rule application unit 102, for example, on the right leaf “y> 0” based on the truth value information stored in the “right side” column of the ID = 1 record of the logical product rule Tb1. From the conditional expression map set, all conditional expressions having a true / false value “false” are extracted. Here, the conditional expression map set of leaf “y> 0” is {((y> 0), true), (¬ (y> 0), false)}, and is combined with the true value “false”. The conditional expression is a single “¬ (y> 0)”.

For example, the truth value rule application unit 102 uses the conditional expression “¬” from the conditional expression map set of leaf “y> 0” as the conditional expression corresponding to the “right side” column of the record of ID = 1 of the logical product rule Tb1. (y> 0) ”is extracted.

Then, the truth value rule application unit 102 generates a conditional expression map that is paired with the truth value “false” stored in the “whole” column for the record of ID = 1 of the logical product rule Tb1. The conditional expression map includes, for example, a conditional expression extracted from the conditional expression map set of the left node and a conditional expression extracted from the conditional expression map set of the right leaf “y> 0” (¬ (y> 0) ).

The truth value rule application unit 102, for example, combines a conditional expression extracted from the conditional expression map set of the left node and a conditional expression extracted from the conditional expression map set of the right leaf with a symbol “∧ ”To join. Then, for example, the truth value rule applying unit 102 adds the truth stored in the “whole” column of the record of ID = 1 of the logical product rule Tb1 to the combined conditional expression of the left node and the conditional expression of the right leaf. The conditional expression map corresponding to the true / false value of ID = 1 is generated by combining the false value “false”.

  In the record of ID = 1 of the logical product rule Tb1, the conditional expression map shown below is generated. The generated conditional expression map corresponding to the true / false value of ID = 1 is temporarily stored in a predetermined area of the main storage unit 12 in association with the ID of the record of the logical product rule Tb1, for example. The

  Conditional expression map with ID = 1: ([((x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z> 1))] || [((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ∧ (y == 1) ∧ (z> 1) ] ∧ ¬ (y> 0), false)

-Logical product rule 2
In the logical product rule Tb1 of FIG. 7H, truth value information (false, 1) is stored in the “left side” column of the record of ID = 2, and truth value information (true, 0) is stored in the “right side” column. Stored. Therefore, the combination rule 2 is applied to the truth value of the conditional expression map set of the left node (logical sum “or”) corresponding to the “left side” column, and the right leaf “y” corresponding to the “right side” column. Combination rule 1 is applied to the truth value of the conditional expression map set of> 0 ”.

  The truth value rule application unit 102, for example, from the conditional expression map set of the left node based on the truth value information stored in the “left side” column of the record of ID = 2 of the logical product rule Tb1. Extract all conditional expressions with the value “false”.

Note that there are four conditional expressions combined with a true / false value “false” in the conditional expression map set of the left node. For example, the truth value rule application unit 102 extracts all the four conditional expressions that are the truth value “false” from the conditional expression map set of the left node in a set form using the symbol “{}”. The conditional expressions extracted from the conditional expression map set of the left node in a set form using the symbol “{}” are as follows.

Conditional expression on the left side of ID = 2: {((x> 0) ∧ ¬ (z == 5)) ∧ ((y == 1) ∧ ¬ (z> 1)), ((x> 0)
∧ ¬ (z == 5)) ∧ (¬ (y == 1) ∧ (z> 1)), ((¬ (x> 0) ∧ (z == 5)) ∧ ((y == 1) ∧ ¬ (z> 1)), ((¬ (x> 0) ∧ (z == 5)) ∧ (¬ (y == 1) ∧ (z> 1))}
Next, the truth value rule application unit 102, for example, the conditional expression of leaf “y> 0” based on the truth value information stored in the “right side” column of the record of ID = 2 of the logical product rule Tb1. From the map set, a conditional expression having a true / false value “true” is combined and extracted using a selection operator. Here, in the conditional expression map set of leaf “y> 0”, there are not a plurality of conditional expressions combined with the true value “true”. Therefore, a single conditional expression “(y> 0)” is extracted without using the selection operator.

For example, the truth value rule application unit 102 uses the conditional expression “(” from the conditional expression map set of leaf “y> 0” as the conditional expression corresponding to the “right side” column of the record of ID = 2 of the logical product rule Tb1. y> 0) ”is extracted.

Then, the truth value rule application unit 102 generates a conditional expression map paired with the truth value “false” stored in the “whole” column for the record of ID = 2 of the logical product rule Tb1. The conditional expression map includes, for example, a conditional expression extracted from the conditional expression map set of the left node in a set form, and a conditional expression extracted from the conditional expression map set of the right leaf “y> 0” (y> 0 ).

For example, the truth value rule application unit 102 calculates each of four conditional expressions extracted from the conditional expression map set of the left node in a set form and the conditional expressions extracted from the conditional expression map set of the right leaf. , Using the symbol “∧” representing the joint. Then, the truth value rule application unit 102 stores, for example, the combined expression of the left node and the conditional expression of the right leaf in the “whole” column of the record of ID = 2 of the AND rule Tb1. A conditional expression map corresponding to the true / false value of ID = 2 is generated by combining the true / false values “false”.

  In the record of ID = 2 of the logical product rule Tb1, a conditional expression map is generated in a set form using the symbol “{}” as in the conditional expression map shown below. Note that the conditional expression map corresponding to the truth value of ID = 2 generated in the set format is associated with the record ID of the logical product rule Tb1, for example, temporarily in a predetermined area of the main storage unit 12. Is remembered.

Conditional expression map with ID = 2: {(((x> 0) ∧ ¬ (z == 5)) ∧ ((y == 1) ∧ ¬ (z> 1)) ∧ (y> 0), false) , (((X> 0) ∧ ¬ (z == 5)) ∧ (¬ (y == 1) ∧ (z> 1)) ∧ (y> 0), false), ((¬ (x> 0 ) ∧ (z == 5)) ∧ ((y == 1) ∧ ¬ (z> 1)) ∧ (y> 0), false), ((¬ (x> 0) ∧ (z == 5) ) ∧ (
¬ (y == 1) ∧ (z> 1)) ∧ (y> 0), false)}

-Logical product rule 3
In the logical product rule Tb1 of FIG. 7H, truth value information (true, 1) is stored in the “left side” column of the record with ID = 3, and truth value information (true, 1) is stored in the “right side” column. Stored. Therefore, the truth value of the conditional expression map set of the left side node (logical sum “or”) corresponding to the “left side” column and the map set of the right side leaf “y> 0” corresponding to the “right side” column The combination rule 2 is applied to each.

  The truth value rule application unit 102, for example, from the conditional expression map set of the left node based on the truth value information stored in the “left side” column of the ID = 3 record of the logical product rule Tb1. Extract all conditional expressions with the value “true”.

Here, there are two types of conditional expressions combined with the true / false value “true” in the conditional expression map set of the left node. For example, the truth value rule application unit 102 extracts, from the conditional expression map set of the left node, all the two conditional expressions that are the true value “true” in a set form using the symbol “{}”. The conditional expressions extracted from the conditional expression map set of the left node in a set form using the symbol “{}” are as follows.
Conditional expression on the left side of ID = 3: {((x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z > 1))), (((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ∧ (y == 1) ∧ (z> 1) )}

Next, the truth value rule application unit 102, for example, the conditional expression of leaf “y> 0” based on the truth value information stored in the “right side” column of the record of ID = 3 of the logical product rule Tb1. From the map set, all conditional expressions with a true / false value “true” are extracted. Here, the conditional expression map set of leaf “y> 0” is {((y> 0), true), (¬ (y> 0), false)}, and is combined with the true value “true”. The conditional expression is a single “(y> 0)”.

For example, the truth value rule application unit 102 uses the conditional expression “(” from the conditional expression map set of leaf “y> 0” as the conditional expression corresponding to the “right side” column of the record with ID = 3 of the logical product rule Tb1. y> 0) ”is extracted.

  Then, the true / false value rule application unit 102 generates a conditional expression map that is paired with the true / false value “true” stored in the “whole” column for the record of ID = 3 of the logical product rule Tb1. The conditional expression map includes, for example, a conditional expression extracted from the conditional expression map set of the left node in a set form, and a conditional expression extracted from the conditional expression map set of the right leaf “y> 0” (y> 0 ).

  The truth value rule application unit 102, for example, obtains each of two types of conditional expressions extracted from the conditional expression map set of the left node in a set form and the conditional expressions extracted from the conditional expression map set of the right leaf. , Using the symbol “∧” representing the joint. Then, the truth value rule application unit 102 stores, for example, the combined expression of the left node and the conditional expression of the right leaf in the “whole” column of the record of ID = 3 of the logical product rule Tb1. A conditional expression map corresponding to the true / false value of ID = 3 is generated by combining the true / false values “true”.

In the record of ID = 3 in the logical product rule Tb1, a conditional expression map is generated in a set form using the symbol “{}” as in the conditional expression map shown below. In addition, the conditional expression map corresponding to the truth value of ID = 3 generated in the set format is associated with the record ID of the logical product rule Tb1 and temporarily stored in a predetermined area of the main storage unit 12, for example. Is remembered.
Conditional expression map of ID = 3: {((x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z> 1)) ∧ (y> 0), true), (((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ∧ (y == 1 ) ∧ (z> 1)
∧ (y> 0), true)}

Conditional expression map set Based on the conditional expression map generated for each record of the logical product rule Tb1 in FIG. 7H, processing of the highest node that connects the node arranged on the left side of the same hierarchy and the leaf arranged on the right side A set of conditional expression maps reflecting the truth value for the process is generated. The truth value rule application unit 102 generates, for example, a conditional expression map set shown in a block D11 in FIG. 7H.

For example, the truth value rule application unit 102 combines the conditional expression maps generated for each record of the logical product rule Tb1, and generates the following conditional expression map set related to the processing process of the highest node. The conditional expression map set shown below corresponds to, for example, a combined expression of each leaf reflecting a true / false value in each processing process for the determination expression C1. The truth value rule application unit 102 stores the following conditional expressions in the conditional expression map set DB 203 in association with, for example, the identification numbers of the determination expressions to be processed.

  Conditional expression map set of judgment formula C1: {([(x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ ( z> 1))] || [((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ∧ (y == 1) ∧ (z> 1)] ∧ ¬ (y> 0), false), ((x> 0) ∧ ¬ (z == 5) ∧ (y == 1) ∧ ¬ (z> 1) ∧ (y> 0), false ), ((X> 0) ∧ ¬ (z == 5) ∧ ¬ (y == 1) ∧ (z> 1) ∧ (y> 0), false), (¬ (x> 0) ∧ (z == 5) ∧ (y == 1) ∧ ¬ (z> 1) ∧ (y> 0), false), (¬ (x> 0) ∧ (z == 5) ∧ ¬ (y == 1) ∧ (z> 1) ∧ (y> 0), false), ((x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬¬y (y = = 1) ∧ (z> 1)) ∧ (y> 0), true), (((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5) ) ∧ (y == 1) ∧ (z> 1) ∧ (y> 0), true)}

[Test case generation process]
Returning to the explanatory diagram illustrated in FIG. 6, the test case generation unit 103, for example, based on the conditional expression map set for the determination formula C1 generated by the truth value rule application unit 102, the test coverage of MC / DC Generate conditional expressions for test cases that satisfy The test case conditional expression generated by the test case generating unit 103 and satisfying the MC / DC test coverage is stored in the test case conditional expression DB 204, for example.

  In the test case generation unit 103, for example, for each conditional expression map included in the conditional expression map set for the determination expression C1, a conditional expression that reflects the truth value of the processing process of the determination expression C1 is extracted. For example, the conditional expression map set generated for the determination expression C1 includes seven conditional expression maps. For this reason, seven types of conditional expressions are extracted from the conditional expression map set generated for the determination expression C1.

  Each extracted conditional expression includes, for example, a conditional expression that is combined using a selection operator. In the following description, a conditional expression obtained by expanding a conditional expression combined using a selection operator is also referred to as a “partial conditional expression”.

  For example, the test case generation unit 103 expands and analyzes the combined conditional expressions using a selection operator, and satisfies the variable relations regarding the partial conditional expressions among the expanded partial conditional expressions. Is selected as a conditional expression of a test case satisfying the MC / DC test coverage. For example, the selection analysis unit 103a performs the expansion / analysis on the conditional expressions combined using the selection operator, and the selection of the partial conditional expression satisfying the variable relation for the partial conditional expression among the expanded partial conditional expressions. Is called.

  For example, the test case generation unit 103 delivers the conditional expression selected as the test case satisfying the test coverage of MC / DC and the partial conditional expression satisfying the selected variable relationship to the satisfiability determination unit 104. In the satisfiability determination unit 104, for example, based on the conditional expressions and partial conditional expressions of the test case delivered from the test case generating unit 103, combinations (test sets) of variable values included in the conditional expressions are obtained. Generated.

(Extraction of conditional expressions for each conditional expression map)
The test case generation unit 103 refers to, for example, the conditional expression map set DB 203 and acquires the conditional expression map set associated with the determination target expression. The acquired conditional expression map set is temporarily stored in a predetermined area of the main storage unit 12, for example.

  For example, the test case generation unit 103 extracts a conditional expression reflecting the process of the determination expression for each conditional expression map from the acquired conditional expression map set. Then, for example, the test case generation unit 103 generates a test case conditional expression table for the processing target determination expression based on the conditional expression extracted for each conditional expression map of the conditional expression map set. The generated test case conditional expression table is stored in the test case conditional expression DB 204, for example.

  FIG. 8A illustrates an example of a test case conditional expression table for the determination formula C1. Since the conditional expression map set generated for the determination expression C1 includes seven conditional expression maps, seven conditional expressions are extracted.

  The test case conditional expression table illustrated in FIG. 8A includes a “No” column and a “Test Case Conditional Expression” column. The “No” column stores a number corresponding to the combination order of the conditional expression maps in the conditional expression map set of the target determination expression. In the “conditional expression of test case” column, a conditional expression reflecting the processing process is stored for each conditional expression map. The conditional expressions stored in the “conditional expression of test case” column include, for example, conditional expressions combined by a selection operator.

In the table example of FIG. 8A, for example, the following seven conditional expressions are stored in records corresponding to the respective numbers in the order of the numbers stored in the “No” column. As shown in the table example of FIG. 8A, it can be seen that the conditional expressions combined using the selection operator are stored in the records of No = 1, 6, and 7.
Conditional expression of No = 1: [(x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z> 1 ))] || [((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ∧ (y == 1) ∧ (z> 1)] ∧ ¬ (y> 0)
Conditional expression of No = 2: (x> 0) ∧ ¬ (z == 5) ∧ (y == 1) ∧ ¬ (z> 1) ∧ (y> 0)
Conditional expression of No = 3: (x> 0) ∧ ¬ (z == 5) ∧ ¬ (y == 1) ∧ (z> 1) ∧ (y> 0)
Conditional expression of No = 4: ¬ (x> 0) ∧ (z == 5) ∧ (y == 1) ∧ ¬ (z> 1) ∧ (y> 0)
Conditional expression of No = 5: ¬ (x> 0) ∧ (z == 5) ∧ ¬ (y == 1) ∧ (z> 1) ∧ (y> 0)
Conditional expression of No = 6: ((x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z> 1 )) ∧
(y> 0)
Conditional expression of No = 7: ((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ∧ (y == 1) ∧ (z> 1 ) ∧ (y> 0)

(Selective analysis processing)
The selection analysis unit 103a of the test case generation unit 103 refers to, for example, the test case conditional expression DB 204 and extracts a conditional expression including a selection operator from the test case conditional expression table generated by the determination expression to be processed. To do. The extracted conditional expression is temporarily stored in a predetermined area of the main storage unit 12 in association with the record number (No) of the test case conditional expression table storing the conditional expression, for example. For example, in the test case conditional expression table illustrated in FIG. 8A, the conditional expressions of records No = 1, 6, and 7 are extracted in order.

  For example, the selection analysis unit 103a expands the extracted conditional expressions and extracts the partial conditional expressions that are in a selection relationship connected by the selection operator. Extraction of subconditional expressions that are in a selective relationship is performed by, for example, preferentially searching and expanding the subconditional expressions arranged on the left side in the deep (early) order of the operation sequence enclosed by the symbol “()”. The partial conditional expression obtained as above is extracted. The extracted partial conditional expression having a selection relationship is temporarily stored in a predetermined area of the main storage unit 12, for example.

  FIG. 8B (1) and FIG. 8 (2) illustrate an explanatory diagram of the selection analysis process. The conditional expressions to be subjected to the selective analysis processing illustrated in FIGS. 8B (1) and (2) are the conditional expressions stored in the record No = 1 in the test case conditional expression table illustrated in FIG. 8A. 8B (1) and 8 (2), the rectangular area surrounded by the alternate long and short dash line is a condition connected by the selection operator on the left side (left term) in the hierarchical relationship of the selection operators. Represents an expression. In the explanatory diagram illustrated in FIG. 8B (1), a rectangular area surrounded by a broken line represents a conditional expression on the left side (left term) of the conditional expression linked by the selection operator. Similarly, in the explanatory diagram illustrated in FIG. 8B (2), a rectangular area surrounded by a thick broken line represents a conditional expression on the right side (right term) of the conditional expression linked by the selection operator.

  In the conditional expression illustrated in FIG. 8B (1), the selection analysis unit 103a selects the left (left term) selection operator in the hierarchical relationship of the selection operators, for example, as shown in a rectangular region surrounded by a one-dot chain line. Search for the conditional expression linked by. For example, the selection analysis unit 103a may use the conditional expression “(x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z > 1)) ”.

  Then, for example, when the searched conditional expression includes a selection operator, the selection analysis unit 103a displays the left side of the conditional expressions combined by the selection operator as shown in a rectangular area surrounded by a broken line. Specify the conditional expression. For example, the selection analysis unit 103a identifies the conditional expression “(y == 1) ¬ (z> 1)” on the left side among the conditional expressions combined by the selection operator.

Then, for example, the selection analysis unit 103a expands the identified left-side conditional expression in combination with the arithmetic relation of the left-side conditional expression searched for in the higher-order selection relationship, and expands with the left priority as the condition No = 1. Extract the partial conditional expression for the conditional expression of the record. The extracted partial conditional expressions are as follows.
-Partial conditional expression 1: (x> 0) ∧ (z == 5) ∧ (y == 1) ∧ ¬ (z> 1) ∧ ¬ (y> 0)

  For example, regarding the extracted partial conditional expression 1, the selection analysis unit 103a does not include any contradiction in each variable relation included in the partial conditional expression 1, and whether there is a combination of variable values satisfying each variable relation. Judgment is made. Whether or not there is a combination of variable values that satisfy each variable relationship for the extracted partial conditional expression is performed by the satisfiability determination unit 104, for example. For example, the selection analysis unit 103 a delivers the extracted partial conditional expression 1 to the satisfiability determination unit 104.

  In the satisfiability determination unit 104, for example, a combination of variable values that satisfies each variable relationship is generated based on the partial conditional expression delivered from the selection analysis unit 103a. The satisfiability determination unit 104, for example, can generate a combination of variable values satisfying each variable relationship when the partial conditional expression delivered from the selection analysis unit 103a does not include any contradiction and can generate variable value combinations. The combination is returned to the selection analysis unit 103a. On the other hand, the satisfiability determination unit 104 cannot generate a combination of variable values that satisfy each variable relationship when the partial conditional expression delivered from the selection analysis unit 103a includes a contradiction. Is returned to the selection analysis unit 103a.

For example, in the case of the partial conditional expression 1, since the variable z includes a contradiction relationship with “(z == 5) ∧ ¬ (z> 1)”, the satisfiability determination unit 104 determines each of the partial conditional expressions C1. A combination of variable values that satisfy the variable relationship cannot be generated. For this reason, for example, the satisfiability determination unit 104 returns a response “unsatisfiable” to the selection analysis unit 103a that has delivered the partial conditional expression C1.

  For example, the selection analysis unit 103a determines the satisfiability of the extracted partial conditional expression based on the response from the satisfiability determination unit 104. For example, when the response “unsatisfiable” is returned to the partial conditional expression delivered to the satisfiability determination unit 104, the selection analysis unit 103a determines that the extracted partial conditional expression is unsatisfiable. . On the other hand, for example, when the combination of the generated variable values is returned for the partial conditional expression delivered to the satisfiability determining unit 104, the selection analysis unit 103a determines that the extracted partial conditional expression can be satisfied. judge. For example, in the case of the partial conditional expression 1, since the response “unsatisfiable” is returned, the selection analysis unit 103a determines that the extracted partial conditional expression 1 is unsatisfiable.

For example, if the extracted partial conditional expression is unsatisfiable, the selection analysis unit 103a determines that the conditional expression “(x> 0) ∧ (z == 5) ∧ ((y == 1) Extract the next partial conditional expression from ∧ ¬ (z> 1) || ¬ (y == 1) ∧ (z> 1)) ”. The extraction of the next partial conditional expression is, for example, shown in FIG.
In the searched conditional expression, the conditional expression “¬ (y == 1) ∧ (z> 1)” connected by the selection operator is used as illustrated in FIG.

  In the conditional expression illustrated in FIG. 8B (2), the selection analysis unit 103a is coupled by a selection operator as shown in a rectangular area surrounded by a thick broken line, for example, from a conditional expression of a dashed-dotted rectangular area. Among the conditional expressions, the right conditional expression “¬ (y == 1) ∧ (z> 1)” is specified.

Then, for example, the selection analysis unit 103a expands the identified right-side conditional expression in combination with the arithmetic relation of the left-side conditional expression searched for in the higher-order selection relation, and the next conditional expression of the record of No = 1. The partial conditional expression is extracted. The extracted partial conditional expressions are as follows.
-Partial conditional expression 2: (x> 0) ∧ (z == 5) ∧ ¬ (y == 1) ∧ (z> 1) ∧ ¬ (y> 0)

  The selection analysis unit 103a, for example, with respect to the extracted partial conditional expression 2, similarly to the partial conditional expression 1, the variable values included in the partial conditional expression 2 include no contradiction and satisfy the variable values. It is determined whether or not a combination exists. The presence / absence of a combination of variable values that satisfy each variable relationship for the partial conditional expression 2 is performed by the satisfiability determination unit 104, for example. For example, the selection analysis unit 103 a delivers the extracted partial conditional expression 2 to the satisfiability determination unit 104.

  In the satisfiability determination unit 104, for example, a combination of variable values that satisfies each variable relationship is generated based on the partial conditional expression delivered from the selection analysis unit 103a. For example, in the partial conditional expression 2, a contradiction is not included in the range relationship for each of the variables x, y, and z. For this reason, in the satisfiability determination unit 104, for example, a combination of variable values generated for the partial conditional expression 2 is returned to the selection analysis unit 103a. For example, the satisfiability determination unit 104 returns a combination of variable values such as “(x, y, z) = (1, −1, 5)” to the selection analysis unit 103a.

  For example, the selection analysis unit 103 a determines the satisfiability of the extracted partial conditional expression 2 based on the response from the satisfiability determination unit 104. In the case of the partial conditional expression A2, for example, since a response “(x, y, z) = (1, −1, 5)” is returned, the selection analysis unit 103a adds the extracted partial conditional expression 2 to On the other hand, it is determined that it can be satisfied.

  When a partial condition expression that can be satisfied is extracted, for example, the selection analysis unit 103a uses the conditional expression stored in the record No = 1 in the test case conditional expression table based on the extracted partial condition expression. Update. For example, the conditional expression “[(x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬¬ (y == 1) ∧ (z> 1 ))] || [((x> 0) ∧ ¬ (z == 5) || ¬ (x> 0) ∧ (z == 5)) ∧ (y == 1) ∧ (z> 1)] ∧ ¬ (y> 0) ”is updated to partial conditional expression“ (x> 0) ∧ (z == 5) ∧ ¬ (y == 1) ∧ (z> 1) ∧ ¬ (y> 0) ” Is done.

For example, the selection analysis unit 103a performs the same process on other conditional expressions of No = 6 and 7 including the selection operator, and the conditions stored in the respective records with the partial conditional expressions determined to be satisfiable. Update the expression. For example, conditional expression “((x> 0) ∧ (z == 5) ∧ ((y == 1) ∧ ¬ (z> 1) || ¬¬y == 1) Ｎｏ before updating No = 6 (z> 1)) ∧ (y> 0) ”is a partial conditional expression“ (x> 0) ∧ (z == 5) ∧
¬ (y == 1) ∧ (z> 1) ∧ (y> 0) ”. Also, for example, conditional expression“ ((x> 0) 更新 ¬ (z = = 5) || ¬ (x> 0) ∧ (z == 5)) ∧ (y == 1) ∧ (z> 1) ∧ (y> 0) ”is the partial conditional expression“ (x> 0) ¬ ¬ (z == 5) ∧ (y == 1) ∧ (z> 1) ∧ (y> 0) ”.

FIG. 8C illustrates a test case conditional expression table example before and after the update. In the test case conditional expression table example before and after the update illustrated in FIG. 8C, the conditional expressions stored in No = 1, 6, and 7 in the test case conditional expression table example illustrated in FIG. 8A are updated with the partial conditional expressions described above. You can see that Note that the test case conditional expression table updated by the selection analysis unit 103a is stored in the test case conditional expression DB 204, for example.

(Test set generation process)
The test case generation unit 103, for example, based on the test case conditional expression table updated by the selection analysis unit 103a, a combination of variable values satisfying each conditional expression reflecting the true / false value of the determination expression to be processed Generate a test set that is The test set is generated by, for example, the satisfaction determination unit 104.

  FIG. 8D illustrates an explanatory diagram of the test case generation process. In the explanatory diagram of FIG. 8D, Tb3 is a test case conditional expression table updated by the selection analysis unit 103a. The test case conditional expression table Tb3 stores seven types of conditional expressions including partial conditional expressions determined to be satisfactory among the conditional expressions combined by the selection operator.

  For example, the test case generation unit 103 refers to the test case conditional expression DB 204 and acquires the test case conditional expression table Tb4 for the determination expression to be processed. The acquired test case conditional expression table Tb4 is temporarily stored in a predetermined area of the main storage unit 12, for example.

  For example, the test case generation unit 103 extracts seven types of conditional expressions stored in each record of the test case conditional expression table Tb4 in the order of record identification numbers, and satisfies the extracted record conditional expressions. To hand over.

  For example, the satisfiability determination unit 104 generates a test set that is a combination of variable values that satisfy the variable relationship included in the conditional expression based on the conditional expression delivered from the test case generation unit 103. The satisfiability determination unit 104 delivers a test set, which is a combination of generated variable values, to the test case generation unit 103, for example. The test case generation unit 103 temporarily associates the test set delivered from the satisfiability determination unit 104 with, for example, a record No in the test case conditional expression table Tb4 in a predetermined area of the main storage unit 12. Remember.

  The satisfiability determination unit 104 detects, for example, a join operator “∧” included in the conditional expression, and extracts a variable condition linked by the join operator. For example, in the conditional expression of the record of No = 1 in the test case conditional expression table Tb4 illustrated in FIG. 8D, the satisfiability determining unit 104 uses the variable condition “(x> 0) combined by the logical operator“ ∧ ”. ”,“ (Z == 5) ”,“ ¬ (y == 1) ”,“ (z> 1) ”,“ ¬ (y> 0) ”are extracted.

The satisfiability determination unit 104 identifies, for example, variables “x”, “y”, “z” from each extracted variable condition, and represents a magnitude relationship such as “>”, “<”, “==” Numeric value “0” combined with
, “1” etc. are specified. Then, the satisfiability determining unit 104, for example, from the magnitude relationship with the numerical value for each variable and the logical relationship with the negation operator “¬t” added to each variable condition,
Variable values satisfying the variable conditions “(x> 0)”, “(z == 5)”, “¬ (y == 1)”, “(z> 1)”, “¬ (y> 0)” Generate a combination of

For example, the satisfiability determining unit 104 generates “x = 1” as a variable value that satisfies the variable condition “(x> 0)”, and the variable conditions “¬ (y == 1)”, “¬ (y > 0) ”as a variable value that satisfies“ y = -1 ”
Is generated. In addition, for example, the satisfiability determination unit 104 performs the variable conditions “(z == 5)”, “(z> 1)
"Z = 5" is generated as a variable value that satisfies "". Then, the satisfiability determination unit 104, for example, "x = 1, y =-as a sufficiency solution that satisfies the variable relationship of the passed conditional expression. A combination of variable values such as “1, z = 5” is delivered to the test case generation unit 103.

  In the test case generation unit 103 and the satisfiability determination unit 104, for example, the same processing is repeated for the conditional expression of record No = 2-7 in the test case conditional expression table Tb3 illustrated in FIG. A satisfactory solution is generated. For example, the test case generation unit 103 generates the test case table Tb4 illustrated in FIG. 8D based on the satisfaction solution delivered from the satisfaction possibility determination unit 104.

Note that the satisfiability determination unit 104 returns a response “unsatisfiable” to the test case generation unit 103 when a combination of variable values that satisfy each variable relationship cannot be generated from the delivered conditional expression. For example, the conditional expression “¬ (x> 0) ∧ (z == 5) ∧ (y == 1) ¬ ¬ (z> 1) ∧ (y> 0) of record No = 4 in the test case conditional expression table Tb3 "" For the variable z "(z == 5)
矛盾 ¬ (z> 1) "is included. For this reason, the satisfiability determining unit 104 tests the response" unsatisfiable "for the conditional expression of record No = 4 in the test case conditional expression table Tb3. It will be returned to the case generation unit 103.

For example, when a response such as “unsatisfiable” is returned, the test case generation unit 103 stores information such as “UNSAT (no satisfied solution)” in the record of No in the corresponding conditional expression,
Generate a test case table. In the test case table Tb4 illustrated in FIG. 8D, for example, information such as “UNSAT (no satisfactory solution)” is stored in the record No = 4.

For example, the test case generation unit 103 stores the generated test case table in the test case DB 205. Then, for example, the test case generation unit 103 outputs the test case C2 by excluding the No record in which “UNSAT (no satisfactory solution)” is stored from the generated test case table.

Satisfactory solutions (test sets) output as the test case C2 are six test sets excluding records in which “UNSAT (no satisfactory solution)” is stored in the test case conditional expression table Tb3. In other words, the information processing apparatus 10 according to the present embodiment covers the MC / DC test coverage that improves the satisfyability of n + 1 or more types, with the minimum number being n + 1, with respect to the determination formula for the condition number n to be processed. A test case satisfying the degree can be generated and output.

[Process flow]
(Overall processing)
Hereinafter, a test case generation process that satisfies the MC / DC test coverage of the information processing apparatus 10 according to the present embodiment will be described with reference to flowcharts illustrated in FIGS. 9A to 9H. FIGS. 9A to 9B are examples of flowcharts illustrating the entire test case generation process that satisfies the test coverage of MC / DC.

  In the flowchart illustrated in FIG. 9A, the start of the test case generation process that satisfies the test coverage of MC / DC can be exemplified by, for example, the acceptance of the determination formula of the program to be tested. For example, the information processing apparatus 10 analyzes the received determination expression, and includes a logical operator included in the determination expression, each conditional expression coupled by the logical operator, and a hierarchical relationship of processing processes for each conditional expression by the logical operator. Is identified. Then, the information processing apparatus 10 converts the hierarchical logical operation relationship of each conditional expression of the received judgment expression into a binary tree structure based on, for example, the identified logical operator, each conditional expression, and the hierarchical relationship of the processing processes. (S1). Note that the binary tree structure conversion processing for the accepted determination formula has been described with reference to FIG. 7A.

The information processing apparatus 10 generates, for example, a determination expression binary tree for the determination expression to be processed as a result of the processing of S1. The generated judgment formula binary tree is stored in the judgment formula binary tree DB 202 in association with the identification information of the accepted judgment formula, for example.

  The information processing apparatus 10, for example, a conditional expression map for a processing process related to a hierarchical logical operation relationship based on a binary tree structure (determination expression binary tree) for a determination target expression generated in the process of S <b> 1. A set is generated (S2). For example, the information processing apparatus 10 performs a true / false rule application process that is a basic rule of logical product and logical sum in which a combination rule is reflected on a node relation and a node / leaf relation hierarchized in a binary tree structure. By applying it, a set of conditional expression maps is generated. The true / false value rule application processing in S2 will be described in detail with reference to FIGS. 9C-9G.

  For example, the information processing apparatus 10 stores the conditional expression map set for the determination formula generated as a result of the processing of S2 in the conditional expression map set DB 203 in association with the identification information of the received determination formula.

  In the process of S3, the information processing apparatus 10 refers to the conditional expression map set DB 203, for example, and acquires the conditional expression map set generated for the determination expression to be processed. The acquired conditional expression map set is temporarily stored in a predetermined area of the main storage unit 12, for example. Then, for example, the information processing apparatus 10 extracts a conditional expression that reflects the true / false value of the processing process of the determination expression for each conditional expression map included in the conditional expression map set. For example, the information processing apparatus 10 generates a test case conditional expression table for the determination expression to be processed based on the conditional expression extracted for each conditional expression map. Note that the test case conditional expression table generation process based on the conditional expression map set has been described with reference to FIG. 8A.

  Here, the test case conditional expression table generated in the process of S3 includes conditional expressions combined using a selection operator. For example, the information processing apparatus 10 stores a test case conditional expression table including conditional expressions combined using a selection operator in the test case conditional expression DB 204 in association with identification information of a determination target expression.

  In the flowchart illustrated in FIG. 9B, in the processing of S <b> 4 to S <b> 8, the information processing apparatus 10 expands the conditional expressions combined using the selection operator of the test case conditional expression table and generates a partial conditional expression. In the process of S4-S8, for example, the information processing apparatus 10 determines whether there is a variable relationship including contradiction for the generated partial conditional expression.

  In the processing of S4-S8, for example, when the partial conditional expression has a variable relationship including a contradiction, the information processing apparatus 10 generates another partial conditional expression that is in a selective relationship with the partial conditional expression, and selects the selective relationship. It is determined whether there is a variable relationship including contradiction for other partial conditional expressions in (1). On the other hand, the information processing apparatus 10 updates the test case conditional expression table based on the generated partial conditional expression when, for example, the contradiction is not included in the variable relationship of the partial conditional expression. The conditional expressions combined using the selection operator in the test case conditional expression table are updated with the partial conditional expressions that do not contain any contradiction in the variable relationship.

  In the process of S4, the information processing apparatus 10 refers to the test case conditional expression DB 204, for example, and acquires the test case conditional expression table generated in the process of S3. The acquired test case conditional expression table is temporarily stored in a predetermined area of the main storage unit 12, for example. Then, the information processing apparatus 10 determines, for example, whether an unprocessed test case conditional expression exists in the test case conditional expression table.

For example, when there is no unprocessed test case conditional expression in the test case conditional expression table (S4, no), the information processing apparatus 10 proceeds to the process of S9. On the other hand, for example, when there is an unprocessed test case conditional expression in the test case conditional expression table (S4, yes), the information processing apparatus 10 proceeds to the process of S5. In the process of S5, the information processing apparatus 10 extracts a test case conditional expression to be processed from the test case conditional expression table.

  In the process of S6, for example, the information processing apparatus 10 determines whether or not the selection symbol “||” representing the selection operator is included in the test case conditional expression extracted in the process of S5. For example, when the selection symbol “||” representing the selection operator is not included in the test case conditional expression extracted in the process of S5 (S6, no), the information processing apparatus 10 performs the processes of S4-S5. repeat. On the other hand, for example, when the selection symbol “||” representing the selection operator is included in the test case conditional expression extracted in the process of S5 (S6, yes), the information processing apparatus 10 proceeds to the process of S7. To do.

  In the processing of S7, for example, the information processing apparatus 10 expands the test case conditional expression based on the selection relationship, and generates a partial conditional expression (test case selection processing). Then, the information processing apparatus 10 determines, for example, whether or not the generated partial conditional expression has a variable relationship including a contradiction, and acquires a partial conditional expression that does not include a contradiction in the variable relationship as a selected test case conditional expression. Details of the processing in S7 will be described with reference to FIG. 9H.

  In the process of S8, the information processing apparatus 10 updates the test case conditional expression in the corresponding test case conditional expression table, for example, based on the partial conditional expression acquired as the test case selected in the process of S7. The updated test case conditional expression table is stored in the test case conditional expression DB 204, for example.

  In the processing of S9-S12, the information processing apparatus 10 uses, for example, a test set that is a combination of variable values that satisfies the test coverage of MC / DC based on the test case conditional expressions stored in the test case conditional expression table. Is generated. And the information processing apparatus 10 produces | generates a test case table based on the test set produced | generated for every test case conditional expression, for example. The generated test case table is stored in the test case DB 205, for example.

  In the process of S9, the information processing apparatus 10 refers to the test case conditional expression DB 204, for example, and acquires the test case conditional expression table updated in the process of S8. The acquired test case conditional expression table is temporarily stored in a predetermined area of the main storage unit 12, for example. Then, the information processing apparatus 10 determines, for example, whether an unprocessed test case conditional expression exists in the test case conditional expression table. Note that the test case conditional expression table updated in the process of S8 does not include the conditional expression combined by the selection operator.

  For example, when there is no unprocessed test case conditional expression in the test case conditional expression table (S9, no), the information processing apparatus 10 proceeds to the process of S12. On the other hand, for example, when there is an unprocessed test case conditional expression in the test case conditional expression table (S9, yes), the information processing apparatus 10 proceeds to the process of S10. In the process of S10, the information processing apparatus 10 extracts a test case conditional expression to be processed from the test case conditional expression table.

  In the process of S11, for example, the information processing apparatus 10 passes the test case conditional expression extracted in the process of S10 to the satisfiability determination unit 104, and acquires a satisfactory solution for the test case conditional expression. The information processing apparatus 10 acquires, for example, a test set that is a combination of variable values returned from the satisfiability determination unit 104 as a satisfactory solution for the delivered test case conditional expression. The acquired satisfaction solution is stored in, for example, a test case table.

For example, the information processing apparatus 10 repeats the processes of S9 to S11 and generates a test case table in the test case conditional expression table acquired in the process of S9. In the process of S12, for example, the test case table generated based on the test case conditional expression table acquired from the test case conditional expression DB 204 in the process of S9 is returned to the information processing apparatus 10.

  For example, the information processing apparatus 10 stores the test case table returned in the process of S12 in the test case table DB 205 in association with the identification information of the determination target expression. In addition, the process of S10-S12 was demonstrated in FIG. 8D.

  Here, the processing of S1 executed by the information processing apparatus 10 is an acquisition step of acquiring the operator in the highest hierarchy in the hierarchy based on the combination of the operator in the logical expression related to the determination and the operation target expression. It is an example. Further, the CPU 11 or the like of the information processing apparatus 10 performs the process of S1 as an example of an acquisition unit that acquires the operator of the highest hierarchy in the hierarchy based on the combination of the operator in the logical expression related to the determination and the operation target expression. Execute.

  Further, the processing of S2 executed by the information processing apparatus 10 is a calculation target expression that satisfies a predetermined relationship between a true / false value of a calculation result by an operator and a true / false value of a calculation target expression to be calculated by the operator. This is an example of an analysis step in which when a plurality of calculation target expressions that are specified and in a predetermined relationship are specified, a plurality of calculation target expressions are held and a truth value of the calculation target expression is obtained. Further, the CPU 11 or the like of the information processing apparatus 10 specifies a calculation target expression that satisfies a predetermined relationship between a true / false value of a calculation result by the operator and a true / false value of a calculation target expression that is a calculation target by the operator. When a plurality of calculation target expressions related to each other are specified, the process of S2 is executed as an example of an analysis unit that holds a plurality of specified calculation target expressions and obtains a truth value of the calculation target expression.

  Further, the processing of S4 to S8 executed by the information processing apparatus 10 determines the satisfiability of the calculation target formula for a plurality of calculation target formulas having a predetermined relationship, and selects the calculation target formula determined to be satisfiable. It is an example of a selection step. Further, the CPU 11 or the like of the information processing apparatus 10 determines the satisfiability of the calculation target formula for a plurality of calculation target formulas having a predetermined relationship, and selects S4 as an example of a selection unit that selects the calculation target formula determined to be satisfiable. -The process of S8 is executed.

(True value rule application processing)
With reference to the flowchart illustrated in FIGS. 9C to 9G, the true / false value rule application processing of S2 illustrated in FIG. 9A will be described. The process of S21-S25 illustrated in FIG. 9C, the process of S31-S44 illustrated in FIGS. 9D-9E, and the process of S51-S64 illustrated in FIGS. 9F-9G have been described with reference to FIGS. 7B-7H, for example.

  In the flowchart illustrated in FIG. 9C, the information processing apparatus 10 determines that the processing target route is a leaf in a binary tree structure, for example (S21). For example, when the route to be processed is a leaf (S21, yes), the information processing apparatus 10 proceeds to the process of S25. In the processing of S25, for example, the information processing apparatus 10 generates a conditional expression map set {(leaf, true), (¬ (leaf), false)} for the processing target leaf, and performs the test case generation processing during processing. return.

  On the other hand, for example, when the route to be processed is not a leaf (S21, no), the information processing apparatus 10 proceeds to the processing of S22 and determines that the route to be processed is an “or” operator. For example, when the route to be processed is an “or” operator (S22, yes), the information processing apparatus 10 proceeds to the process of S24. On the other hand, for example, when the route to be processed is not the “or” operator (S22, no), the information processing apparatus 10 proceeds to the process of S23.

In the processing of S23, for example, the information processing apparatus 10 applies the truth value rule application processing to the left child tree (left node or leaf) related to the logical product node processing to generate a conditional expression map set. Further, for example, the information processing apparatus 10 applies a truth value rule application process to a right child tree (right node or leaf) related to a logical product node process to generate a conditional expression map set.

  Then, the information processing apparatus 10, for example, from the conditional expression map set generated for each of the left and right child trees, the conditional expression map reflecting the truth value for the processing process (AND operator processing) related to the logical product node Generate a set and return it to the test case generation process being processed. Details of the processing of S23 will be described with reference to FIGS. 9D-9E.

In the processing of S24, for example, the information processing apparatus 10 applies the truth value rule application processing to the left child tree (left node or leaf) related to the logical sum node processing to generate a conditional expression map set. Further, for example, the information processing apparatus 10 applies a truth value rule application process to a right child tree (right node or leaf) related to a logical sum node process to generate a conditional expression map set.
Then, the information processing apparatus 10, for example, from the conditional expression map set generated for each of the left and right child trees, the conditional expression map that reflects the truth value for the processing process (OR operator processing) related to the logical sum node. Generate a set and return it to the test case generation process being processed. Details of the processing of S24 will be described with reference to FIGS. 9F-9G.

  In the processing of S23 to S24, the generation of the conditional expression map set to which the truth value rule is applied is performed based on, for example, the logical product rule Tb1 and the logical sum rule Tb2 stored in the truth value rule DB 201. The combination rule is reflected in the logical product rule Tb1 and the logical sum rule Tb2 stored in the truth value rule DB 201.

  Here, the processing of S21 to S25 executed by the information processing apparatus 10 is an arithmetic expression that is combined with the operator of the highest hierarchy in the hierarchy using the truth value of the arithmetic expression that satisfies the predetermined relationship. It is an example of the step which applies an acquisition step and an analysis step to the logical expression. Further, the CPU 11 or the like of the information processing apparatus 10 obtains a logical expression below the calculation target expression that is combined by the operator of the highest hierarchy in the hierarchy using the truth value of the calculation target expression that satisfies the predetermined relationship. As an example of means for applying the analysis step, the processing of S21 to S25 is executed.

AND Operator Processing The AND operator processing of S23 illustrated in FIG. 9C will be described with reference to the flowchart illustrated in FIGS. 9D-9E. In the following description, the conditional expression map set generated by the left child tree related to the logical node processing is also referred to as “left map set”, and the conditional expression map set generated by the right child tree is referred to as “right map set”. Also referred to.

In the flowchart illustrated in FIG. 9D, for example, the information processing apparatus 10 extracts all the conditional expression maps that are paired with the true value “true” from the left map set, and based on the extracted conditional expressions of the conditional expression maps. Thus, the join expression “leftcond1” joined by the selection operator is generated (S31).
. The generated combined expression “leftcond1” is as follows. The identifier “n” added to the conditional expression represents the quantity of the extracted conditional expression map.

Leftcond1: = (conditional expression 1 || ... | conditional expression n)
In the process of S32, the information processing apparatus 10, for example, from the right map set, the truth value “false”
Are extracted, and “rightcond1” is generated based on the extracted conditional expressions of each conditional expression map. When there are a plurality of extracted conditional expression maps, “rightcond1” is expressed as follows using a symbol “{}” representing a set. The identifier “n” added to the conditional expression represents the quantity of the extracted conditional expression map.

Rightcond1: {(conditional expression 1), ..., (conditional expression n)}
In the process of S33, the information processing apparatus 10, for example, “rightcon” generated in the process of S32
It is determined whether there is an unprocessed conditional expression in “d1”. For example, when there is an unprocessed conditional expression in “rightcond1” (S33, yes), the information processing apparatus 10 performs S34. On the other hand, for example, when there is no unprocessed conditional expression in “rightcond1” (S33, no), the information processing apparatus 10 proceeds to the process of S35.

In the process of S34, for example, the information processing apparatus 10 extracts the conditional expression (rightcond1) from “rightcond1” generated in the process of S32, and extracts the extracted conditional expression (rightcond1) as “leftcond1” and the join operator “∧”. Join with. The conditional expression (rightcond1) includes a plurality of conditional expressions. The combination expression of “leftcond1” and the conditional expression (rightcond1) is represented by “leftcond1∧rightcond1”.

Then, for example, the information processing apparatus 10 generates a conditional expression map (leftcond1 ∧ rightcond1, false) that combines the combined expression “leftcond1∧rightcond1” and the truth value “false” of ID = 1 of the logical product rule Tb1. . For example, the information processing apparatus 10 adds the generated conditional expression map to the return conditional expression map set.

  For example, the information processing apparatus 10 can generate a conditional expression map corresponding to the record of ID = 1 of the logical product rule Tb1 by repeating the processing of S33 to S34.

  Next, the information processing apparatus 10 generates a conditional expression map corresponding to the record of ID = 2 of the logical product rule Tb1 by, for example, the processing of S35 to S38.

  For example, the information processing apparatus 10 extracts all the conditional expression maps that are paired with the true / false value “true” from the right map set, and is combined with a selection operator based on the extracted conditional expressions of each conditional expression map. The combined expression “rightcond2” is generated (S35). The generated join expression “rightcond2” is as follows. The identifier “n” added to the conditional expression represents the quantity of the extracted conditional expression map.

Rightcond2: = (conditional expression 1 || ... | conditional expression n)
In the process of S36, the information processing apparatus 10, for example, from the left map set, the truth value “false”
Are extracted, and “leftcond2” is generated based on the extracted conditional expressions in each conditional expression map. When there are a plurality of extracted conditional expression maps, “leftcond2” is expressed as follows using a symbol representing a set. The identifier “n” added to the conditional expression represents the quantity of the extracted conditional expression map.

Leftcond2: {(conditional expression 1), ..., (conditional expression n)}
In the process of S37, the information processing apparatus 10, for example, determines whether an unprocessed conditional expression exists in “leftcond2” generated in the process of S36. For example, when an unprocessed conditional expression exists in “leftcond2” (S37, yes), the information processing apparatus 10 proceeds to the process of S38. On the other hand, for example, when there is no unprocessed conditional expression in “leftcond2” (S37, no), the information processing apparatus 10 proceeds to the process of S39 in the flowchart illustrated in FIG. 9E.

In the process of S38, for example, the information processing apparatus 10 extracts the conditional expression (leftcond2) from “leftcond2” generated in the process of S36, and extracts the extracted conditional expression (leftcond2) as “rightcond2” and the join operator “∧”. Join with. Similar to S34, the conditional expression (leftcond2) includes a plurality of conditional expressions. The combination expression of “leftcond2” and the conditional expression (rightcond2) is represented by “leftcond2∧rightcond2”.

Then, for example, the information processing apparatus 10 generates a conditional expression map (leftcond2 2 rightcond2, false) that combines the combination expression “leftcond2∧rightcond2” and the truth value “false” of ID = 2 of the logical product rule Tb1. . For example, the information processing apparatus 10 adds the generated conditional expression map to the return conditional expression map set.

  Next, the information processing apparatus 10 generates a conditional expression map corresponding to the record of ID = 3 of the logical product rule Tb1 by, for example, the processing of S39 to S43 in the flowchart illustrated in FIG. 9E.

In the flowchart illustrated in FIG. 9E, for example, the information processing apparatus 10 extracts all conditional expression maps that are paired with a true value “true” from the left map set, and based on the extracted conditional expressions of each conditional expression map. Then, “leftcond3” is generated (S39). When there are a plurality of extracted conditional expression maps, “leftcond3” is expressed as follows using a symbol representing a set.
The identifier “n” added to the conditional expression represents the quantity of the extracted conditional expression map.

Leftcond3: {(conditional expression 1), ..., (conditional expression n)}
Similarly, the information processing apparatus 10 extracts, for example, all the conditional expression maps that are paired with the true / false value “true” from the right map set, and sets “rightcond3” based on the extracted conditional expressions of each conditional expression map. "Is generated (S40). When there are a plurality of extracted conditional expression maps, “rightcond3” is expressed as follows using a symbol representing a set. The identifier “n” added to the conditional expression represents the quantity of the extracted conditional expression map.

Rightcond3: {(conditional expression 1), ..., (conditional expression n)}
In the process of S41, the information processing apparatus 10 determines whether an unprocessed conditional expression exists in “leftcond3” generated in the process of S39, for example. For example, when an unprocessed conditional expression exists in “leftcond3” (S41, yes), the information processing apparatus 10 proceeds to the process of S42. On the other hand, for example, when there is no unprocessed conditional expression in “leftcond3” (S41, no), the information processing apparatus 10 proceeds to the process of S44.

  In the process of S42, the information processing apparatus 10 determines whether an unprocessed conditional expression exists in “rightcond3” generated in the process of S40, for example. For example, when an unprocessed conditional expression exists in “rightcond3” (S42, yes), the information processing apparatus 10 proceeds to the process of S43. On the other hand, for example, when there is no unprocessed conditional expression in “rightcond3” (S42, no), the information processing apparatus 10 repeats the process of S41.

In the process of S43, for example, the information processing apparatus 10 extracts the conditional expression (leftcond3) from “leftcond3” generated in the process of S39, and extracts the conditional expression (rightcond3) from “rightcond3” generated in the process of S40. . The information processing apparatus 10 combines, for example, the extracted conditional expressions, and combines the combined conditional expressions with the truth value “true” of ID = 3 of the logical product rule Tb1 (leftcond3 ∧ rightcond3, true) is generated. For example, the information processing apparatus 10 adds the generated conditional expression map to the return conditional expression map set.

  For example, the information processing apparatus 10 can generate a conditional expression map corresponding to the record of ID = 3 of the logical product rule Tb1 by repeating the processing of S41 to S43.

  In the process of S44, for example, the information processing apparatus 10 returns the return conditional expression map set in which the conditional expression map generated in the processes of S34, S38, and S43 is stored to the true / false value rule application process being processed. To do.

OR Operator Processing The OR operator processing in S24 illustrated in FIG. 9C will be described with reference to the flowchart illustrated in FIGS. 9F-9G. In the following description, similarly to the AND operator processing, the conditional expression map set generated by the left child tree related to the logical sum node processing is also referred to as “left map set”, and the conditional expression generated by the right child tree. The map set is also referred to as “right map set”.

  For example, the information processing apparatus 10 generates a conditional expression map corresponding to the record of ID = 1 of the logical sum rule Tb2 by the processing of S51 to S54 in the flowchart illustrated in FIG. 9F. Similarly, the information processing apparatus 10 generates a conditional expression map corresponding to the record of ID = 2 of the logical sum rule Tb2 by the processing of S55 to S58 in the flowchart illustrated in FIG. 9F, for example. Further, for example, the information processing apparatus 10 generates a conditional expression map corresponding to the record of ID = 3 of the logical sum rule Tb2 by the processing of S59 to S63 in the flowchart illustrated in FIG. 9G.

In the flowchart illustrated in FIG. 9F, for example, the information processing apparatus 10 extracts all the conditional expression maps that are paired with the true / false value “false” from the right map set, and based on the extracted conditional expressions of the conditional expression maps. Thus, the join expression “rightcond1” joined by the selection operator is generated (S51). The generated join expression “rightcond1” is as follows. The identifier “n” added to the conditional expression represents the quantity of the extracted conditional expression map.

Rightcond1: = (conditional expression 1 || ... || conditional expression n)
In the process of S52, for example, the information processing apparatus 10 extracts all the conditional expression maps that are paired with the true value “true” from the left map set, and, based on the extracted conditional expressions of the conditional expression maps, “ leftcond1 ”is generated. When there are a plurality of extracted conditional expression maps, “leftcond1” is expressed as follows using a symbol “{}” representing a set. The identifier “n” added to the conditional expression represents the quantity of the extracted conditional expression map.

Leftcond1: {(conditional expression 1), ..., (conditional expression n)}
In the process of S53, the information processing apparatus 10 determines, for example, whether an unprocessed conditional expression exists in “leftcond1” generated in the process of S52. For example, when an unprocessed conditional expression exists in “leftcond1” (S53, yes), the information processing apparatus 10 proceeds to the process of S54. On the other hand, for example, when there is no unprocessed conditional expression in “leftcond1” (S53, no), the information processing apparatus 10 proceeds to the process of S55.

In the process of S54, for example, the information processing apparatus 10 extracts the conditional expression (leftcond1) from “leftcond1” generated in the process of S52, and extracts the extracted conditional expression (leftcond1) as “rightcond1” and the join operator “∧”. Join with. The conditional expression (leftcond1) includes a plurality of conditional expressions. The combination expression of “leftcond1” and the conditional expression (rightcond1) is represented by “leftcond1∧rightcond1”.

Then, for example, the information processing apparatus 10 generates a conditional expression map (leftcond1 ∧ rightcond1, true) that combines the combination expression “leftcond1∧rightcond1” and the truth value “true” of ID = 1 of the logical sum rule Tb2. . For example, the information processing apparatus 10 adds the generated conditional expression map to the return conditional expression map set.

Next, the information processing apparatus 10 extracts, for example, all the conditional expression maps that are paired with the truth value “false” from the left map set, and selects operators based on the extracted conditional expressions of the respective conditional expression maps. The combined expression “leftcond2” combined in step S55 is generated (S55). The generated combined expression “leftcond2” is as follows. The identifier “n” added to the conditional expression represents the quantity of the extracted conditional expression map.

Leftcond2: = (conditional expression 1 || ... | conditional expression n)
In the process of S56, for example, the information processing apparatus 10 extracts all the conditional expression maps that are paired with the true value “true” from the right map set, and, based on the extracted conditional expressions of the conditional expression maps, “ rightcond2 ”is generated. When there are a plurality of extracted conditional expression maps, “rightcond2” is expressed as follows using a symbol representing a set. The identifier “n” added to the conditional expression represents the quantity of the extracted conditional expression map.

Rightcond2: {(conditional expression 1), ..., (conditional expression n)}
In the process of S57, the information processing apparatus 10 determines whether an unprocessed conditional expression exists in “rightcond2” generated in the process of S56, for example. For example, when an unprocessed conditional expression exists in “rightcond2” (S57, yes), the information processing apparatus 10 proceeds to the process of S58. On the other hand, for example, when there is no unprocessed conditional expression in “rightcond2” (S57, no), the information processing apparatus 10 proceeds to the process of S59 in the flowchart illustrated in FIG. 9G.

In the process of S58, for example, the information processing apparatus 10 extracts the conditional expression (rightcond2) from “rightcond2” generated in the process of S56, and extracts the extracted conditional expression (rightcond2) as “leftcond2” and the join operator “∧”. Join with. The conditional expression (rightcond2) includes a plurality of conditional expressions. The combination expression of “leftcond2” and the conditional expression (rightcond2) is represented by “leftcond2∧rightcond2”.

Then, for example, the information processing apparatus 10 generates a conditional expression map (leftcond2 2 rightcond2, true) that combines the combined expression “leftcond2∧rightcond2” and the truth value “true” of ID = 2 of the logical sum rule Tb2. . For example, the information processing apparatus 10 adds the generated conditional expression map to the return conditional expression map set.

In the flowchart illustrated in FIG. 9G, for example, the information processing apparatus 10 extracts all the conditional expression maps that are paired with the truth value “false” from the left map set, and based on the extracted conditional expressions of the conditional expression maps. Then, “leftcond3” is generated (S59). When there are a plurality of extracted conditional expression maps, “leftcond3” is expressed as follows using a symbol representing a set. The identifier “n” added to the conditional expression represents the quantity of the extracted conditional expression map.

Leftcond3: {(conditional expression 1), ..., (conditional expression n)}
Similarly, the information processing apparatus 10 extracts, for example, all the conditional expression maps that are paired with the true / false value “false” from the right map set, and based on the extracted conditional expressions in each conditional expression map, “rightcond3 "Is generated (S60). When there are a plurality of extracted conditional expression maps, “rightcond3” is expressed as follows using a symbol representing a set. The identifier “n” added to the conditional expression represents the quantity of the extracted conditional expression map.

Rightcond3: {(conditional expression 1), ..., (conditional expression n)}
In the process of S61, the information processing apparatus 10 determines whether an unprocessed conditional expression exists in “leftcond3” generated in the process of S59, for example. For example, when an unprocessed conditional expression exists in “leftcond3” (S61, yes), the information processing apparatus 10 proceeds to the process of S62. On the other hand, for example, when there is no unprocessed conditional expression in “leftcond3” (S61, no), the information processing apparatus 10 proceeds to the process of S64.

  In the process of S62, the information processing apparatus 10 determines, for example, whether an unprocessed conditional expression exists in “rightcond3” generated in the process of S60. For example, when an unprocessed conditional expression exists in “rightcond3” (S62, yes), the information processing apparatus 10 proceeds to the process of S63. On the other hand, for example, when there is no unprocessed conditional expression in “rightcond3” (S62, no), the information processing apparatus 10 repeats the process of S61.

In the process of S63, for example, the information processing apparatus 10 extracts the conditional expression (leftcond3) from “leftcond3” generated in the process of S59, and extracts the conditional expression (rightcond3) from “rightcond3” generated in the process of S60. . Then, the information processing apparatus 10 combines, for example, the extracted conditional expressions, and combines the combined conditional expressions with the truth value “false” of ID = 3 of the logical sum rule Tb2 (leftcond3 ∧ rightcond3, false). For example, the information processing apparatus 10 adds the generated conditional expression map to the return conditional expression map set.

  In the process of S64, for example, the information processing apparatus 10 returns the return conditional expression map set in which the conditional expression map generated in the processes of S54, S58, and S63 is stored to the true / false value rule application process being processed. To do.

(Test case selection process)
The test case selection process of S7 illustrated in FIG. 9B will be described with reference to the flowchart illustrated in FIG. 9H. Note that the processing of S71 to S75 illustrated in FIG. 9H has been described with reference to FIGS. 8B (1) and (2), for example.

  In the flowchart illustrated in FIG. 9H, for example, the information processing apparatus 10 determines whether the depth-first search in which the selection operator “||” is regarded as a branch is completed for the test case conditional expression to be processed. (S71). For example, when the depth-first search with the selection operator regarded as a branch is completed for the test case conditional expression to be processed (S71, yes), the information processing apparatus 10 proceeds to the process of S76. Return the empty conditional expression to the test case generation process. In the information processing apparatus 10, for example, when an empty conditional expression is returned in the process of S <b> 76, it is determined that the test case conditional expression to be processed cannot be satisfied.

  On the other hand, for example, when the depth-first search in which the selection operator is regarded as a branch is not completed for the test case conditional expression to be processed (S71, no), the information processing apparatus 10 performs the process of S72. Transition.

  In the processing of S72, the information processing apparatus 10 performs, for example, a depth-first search that considers the selection operator “||” as a branch for the test case conditional expression to be processed, and expands the selection relationship. The generated partial conditional expression is selected as a test case conditional expression to be processed. Note that, for example, the selection operator is not included in the partial conditional expression generated in the process of S72.

  For example, the information processing apparatus 10 passes the partial conditional expression, which is the test case conditional expression selected in the process of S72, to the satisfiability determining unit 104, and determines the satisfaction of the delivered partial conditional expression (S73). ). Satisfiability of the delivered partial conditional expression is performed by, for example, response information returned from the satisfiability determination unit 104.

  For example, when the combination of empty variable values indicating “unsatisfiable” is returned as response information from the satisfiability determination unit 104, the information processing apparatus 10 satisfies the partial conditional expression to be processed. Judged as impossible. Further, for example, when the test set that is a combination of variable values included in the partial conditional expression is returned as response information from the satisfiability determination unit 104, the information processing apparatus 10 determines the partial conditional expression to be processed. It is determined that it can be satisfied.

  In the process of S74, the information processing apparatus 10 determines the satisfaction of the partial conditional expression selected in the process of S72 based on the determination described above, for example. For example, if the partial conditional expression selected in the process of S72 is unsatisfiable (S74, no), the information processing apparatus 10 proceeds to the process of S71, and selects other partial conditional expressions that are in a selection relationship. The processes of S71 to S74 are repeated.

  On the other hand, for example, when the partial conditional expression selected in the process of S72 can be satisfied (S74, yes), the information processing apparatus 10 proceeds to the process of S75, and uses the selected partial conditional expression as a test case. Return to the test case generation process being processed as a conditional expression. In the test case generation process, for example, the test case conditional expression including the selection operator stored in the test case conditional expression table is updated based on the partial conditional expression returned in the process of S75.

  As described above, the information processing apparatus 10 according to the present embodiment analyzes the determination expression to be tested, and includes the logical operator included in the determination expression, each conditional expression combined with the logical operator, and the logical operator. It is possible to specify the hierarchical relationship of processing processes for each conditional expression. The information processing apparatus 10 according to the present embodiment, for example, uses a binary tree structure to represent a hierarchical logical operation relationship of each conditional expression included in the determination expression based on the identified logical operator, each conditional expression, and the hierarchical relationship of processing processes. Can be converted to

  In addition, the information processing apparatus 10 according to the present embodiment includes a truth table that stores combinations of three true / false values, which are basic rules such as logical product and logical sum, regarding the logical operation relationship of the determination formula. Can do. The information processing apparatus 10 according to the present embodiment applies each of the three combinations of truth values stored in the truth table of the basic rule to the logical operation relationship of the determination formula, thereby It is possible to exclude the combination of the truth value of the conditional expression that is a surplus from the combination of the truth value of the conditional expression.

  In addition, each truth table of the present embodiment does not affect the determination result of the truth value independently (the determination result does not change even if the truth value of the target conditional expression is inverted), A combination rule that combines combinations of conditional expressions with common determination results by using a selection operator can be reflected. Similarly, each truth table of the present embodiment has an independent influence on the determination result of the truth value (if the determination result changes when the truth value of the target conditional expression is inverted), the determination result The combination rule of extracting all combinations of common conditions can be reflected. In the information processing apparatus 10 according to the present embodiment, when extracting a combination of conditional expressions having a common determination result based on a truth table of logical products and logical sums, a combination of conditional expressions having unsatisfiable variable relationships is extracted. Extraction can be prevented.

  The information processing apparatus 10 according to the present embodiment uses each conditional expression related to the logical operation processing process included in the determination expression according to the truth value stored in the truth table of the logical product / logical sum reflecting the combination rule. The true / false relation of can be specified including options. The information processing apparatus 10 can specify, for example, a combination of true / false values of each conditional expression that is a determination result that matches the true / false value stored in the true / false table corresponding to the logical operation of the determination expression. . The combination of the true / false values of the specified conditional expressions includes a plurality of combinations of conditional expressions that are combined by a selection operator and share the true / false values of the determination results.

  The information processing apparatus 10 according to the present embodiment specifies a true / false relation of each upper-level conditional expression linked by a hierarchical uppermost logical operator, and further performs a lower-order arithmetic processing process related to each upper-level conditional expression It is possible to hierarchically (recursively) obtain the truth value relationship of the subordinate conditional expressions for. Then, the information processing apparatus 10 according to the present embodiment expands a plurality of conditional expressions that are in a selective relationship with respect to each conditional expression that is specified in the processing process of the highest-level logical operation, and a conditional expression that has a satisfying variable relationship Can be selected.

As a result, the information processing apparatus 10 according to the present embodiment improves the satisfiability of the determination formula to be tested and eliminates the redundancy, and satisfies the MC / DC test coverage level. A combination of false values can be generated. The information processing apparatus 10 according to the present embodiment responds to the value of the truth value that does not include any contradiction in the variable relationship for each variable included in each conditional expression, from the combination of the generated truth values of the conditional expressions. A combination of variable values can be generated as a test case. In the information processing apparatus 10 according to the present embodiment, a combination of variable values that satisfies a variable relationship can be generated even when a variable common to a plurality of conditional expressions is included.

  The information processing apparatus 10 according to the present embodiment can provide a technique for generating a test case that satisfies the coverage rule of the MC / DC test coverage with improved satisfiability and capable of eliminating redundancy.

<Computer-readable recording medium>
A program for causing a computer or other machine or device (hereinafter, a computer or the like) to realize any of the above functions can be recorded on a recording medium that can be read by the computer or the like. Then, the function can be provided by causing the computer or the like to read and execute the program of the recording medium.

  Here, a computer-readable recording medium is a recording medium that stores information such as data and programs by electrical, magnetic, optical, mechanical, or chemical action and can be read from a computer or the like. Say. Examples of such a recording medium that can be removed from a computer or the like include a flexible disk, a magneto-optical disk, a CD-ROM, a CD-R / W, a DVD, a Blu-ray disk, a DAT, an 8 mm tape, a flash memory, and the like. There are cards. Moreover, there are a hard disk, a ROM, and the like as a recording medium fixed to a computer or the like.

<Others>
The above embodiment further includes an aspect called the following supplementary note. The components included in the following supplementary notes can be combined with the constituents included in the other supplementary notes.
(Appendix 1)
On the computer,
Identifies an operation target expression that satisfies a predetermined relationship between the true / false value of the operation result by the operator and the truth value of the operation target expression to be operated by the operator, and specifies a plurality of operation target expressions having the predetermined relationship An analysis step for holding a plurality of operation target expressions specified and obtaining a true / false value of the operation object expression;
A selection step of determining the satisfiability of the calculation target expression for the plurality of calculation target expressions in the predetermined relationship, and selecting the calculation target expression determined to be satisfiable;
Test case generation program to execute
(Appendix 2)
An acquisition step of acquiring an operator of the highest hierarchy in the hierarchy based on a combination of the operator in the logical expression related to the determination and the operation target expression;
Applying the obtaining step and the analyzing step to a logical expression below the operation target expression that is combined by an operator of the highest hierarchy in the hierarchy using a truth value of the operation expression that satisfies the predetermined relationship When,
The test case generation program according to appendix 1, further executing
(Appendix 3)
The test case generation program according to appendix 1 or appendix 2, wherein the selection step determines satisfiability based on whether or not there is a contradiction in the combination relation between the first sub-expression and the second sub-expression included in the calculation target expression.
(Appendix 4)
In the hierarchy, when a plurality of operators are included in the logical expression, the operator having the higher priority and the operation target expression operated by the operator are arranged in the order of priority of the operation order by the operator. In the calculation target expression that is a hierarchy, and in the priority order of the calculation order by the operator, the next operator and the next calculation target expression calculated by the next operator are The test case generation program according to appendix 2 or appendix 3, formed by positioning the next hierarchy immediately below the upper hierarchy as a hierarchy.
(Appendix 5)
The analysis step does not affect a truth value of an operation result by a first operator included in a first hierarchy, and a truth value of a first operation target expression that is an object of operation by the first operator Is a common value, and there are a plurality of combinations of true / false values of the operation target expression in the second hierarchy immediately below the first hierarchy, a selection operation capable of selecting the plurality of true / false combinations. The test case generation program according to appendix 4, including a step of selecting a combination of true and false values combined by a child and combined by the selection operator.
(Appendix 6)
The analyzing step affects a true / false value of a calculation result by a first operator included in a first hierarchy, and a true / false value of a first calculation target expression to be calculated by the first operator. When there are a plurality of combinations of true / false values of the operation target expression of the second hierarchy immediately below the first hierarchy, all combinations among the plurality of combinations of true / false values The test case generation program according to appendix 4, including a step of selecting.
(Appendix 7)
In the analyzing step, when the operator of the first hierarchy is a product operator, a combination of true / false values of two first operation target expressions to be operated by the product operator is true, false, false Supplementary note 4 to Supplementary note 6, including a step of selecting a combination of true and false values in the second calculation target expression immediately below the first hierarchy so as to be true and true or true Test case generation program described in 1.
(Appendix 8)
In the analyzing step, when the operator of the first hierarchy is a sum operator, the combination of the true / false values of the two first operation target expressions to be operated by the sum operator is true, false, false The supplementary statement according to any one of supplementary notes 4 to 6, including a step of selecting a combination of true and false values in the second calculation target expression immediately below the first hierarchy so as to be true and false or false and false Test case generation program described in 1.
(Appendix 9)
When the operator of the first hierarchy is a product operator, in the combination of true and false or false and true among the two first operation target expressions to be operated by the product operator, The test case generation program according to appendix 7, wherein a calculation target expression whose truth value is true is combined as a selectable calculation target expression with a selection operator.
(Appendix 10)
When the operator of the first hierarchy is a sum operator, in the combination of true and false or false and true among the two first computation target expressions to be computed by the sum operator, The test case generation program according to appendix 8, wherein a calculation target expression whose truth value is false is combined as a selectable calculation target expression with a selection operator.

(Appendix 11)
A method for generating a test case using a computer,
A processor of the computer,
Identifies an operation target expression that satisfies a predetermined relationship between the true / false value of the operation result by the operator and the truth value of the operation target expression to be operated by the operator, and specifies a plurality of operation target expressions having the predetermined relationship In the case where the calculation target expression is retained, a truth value of the calculation target expression is obtained,
Determining satisfiability of the calculation target expression for the plurality of calculation target expressions in the predetermined relationship, and selecting the calculation target expression determined to be satisfiable;
Test case generation method.
(Appendix 12)
Obtain the operator at the highest level in the hierarchy based on the combination of the operator in the logical expression related to the judgment and the operation target expression,
Using the truth value of the operation target expression that satisfies the predetermined relationship, the acquisition and the truth value of the operation target expression are added to the logical expression below the operation target expression combined by the operator of the highest hierarchy in the hierarchy. The test case generation method according to attachment 11, wherein the test case generation method is performed.
(Appendix 13)
Note that selecting the calculation target expression determined to be satisfiable determines satisfiability based on whether or not there is a contradiction in the combination relation between the first partial expression and the second partial expression included in the calculation target expression. Or the test case generation method according to attachment 12.
(Appendix 14)
In the hierarchy, when a plurality of operators are included in the logical expression, the operator having the higher priority and the operation target expression operated by the operator are arranged in the order of priority of the operation order by the operator. In the calculation target expression that is a hierarchy, and in the priority order of the calculation order by the operator, the next operator and the next calculation target expression calculated by the next operator are 14. The test case generation method according to appendix 12 or appendix 13, wherein the test case generation method is formed by positioning the next hierarchy immediately below the higher hierarchy.
(Appendix 15)
Obtaining the true / false value of the calculation target expression does not affect the true / false value of the calculation result by the first operator included in the first hierarchy, and is the first to be calculated by the first operator. When there are a plurality of combinations of true / false values of the calculation target expressions in the second hierarchy immediately below the first hierarchy, the true / false values of the calculation target expressions of 15. The test case generation method according to appendix 14, comprising: combining the combinations with a selectable selection operator, and selecting a combination of true and false values combined with the selection operator.
(Appendix 16)
Obtaining the true / false value of the calculation target expression affects the true / false value of the calculation result by the first operator included in the first hierarchy, and is the first to be calculated by the first operator. When there are a plurality of combinations of true / false values of the calculation target expressions in the second hierarchy immediately below the first hierarchy, the true / false values of the calculation target expressions of 15. The test case generation method according to appendix 14, including selecting all combinations among the combinations.
(Appendix 17)
Obtaining the truth value of the operation target expression means that the truth values of the two first operation object expressions to be operated by the product operator when the operator of the first hierarchy is a product operator. From the supplementary note 14, comprising selecting a combination of true and false values in the second arithmetic expression immediately below the first hierarchy so that the combination of true and false is false and true or true and true The test case generation method according to any one of supplementary notes 16.
(Appendix 18)
The determination of the truth value of the operation target expression means that the truth values of the two first operation target expressions that are the targets of the operation by the sum operator when the operator of the first hierarchy is the sum operator. From the supplementary note 14, including selecting a combination of true and false values in the second operation target expression immediately below the first hierarchy so that the combination becomes true and false, false and true, or false and false The test case generation method according to any one of supplementary notes 16.
(Appendix 19)
When the operator of the first hierarchy is a product operator, in the combination of true and false or false and true among the two first operation target expressions to be operated by the product operator, 18. The test case generation method according to appendix 17, wherein a calculation target expression whose truth value is true is combined with a selection operator as a selectable calculation target expression.
(Appendix 20)
When the operator of the first hierarchy is a sum operator, in the combination of true and false or false and true among the two first computation target expressions to be computed by the sum operator, Item 19. The test case generation method according to appendix 18, wherein a calculation target expression whose truth value is false is combined as a selectable calculation target expression with a selection operator.

(Appendix 21)
The true / false values of the calculation result by the operator and the true / false values of the calculation target expression to be calculated by the operator satisfy a predetermined relationship, and a plurality of the calculation target expressions in the predetermined relationship Analysis means for obtaining a true / false value of the calculation target expression so that one calculation target expression can be selected from among them,
Selection means for determining satisfiability of the calculation target expression for the plurality of calculation target expressions having the predetermined relationship, and selecting the calculation target expression determined to be satisfiable;
A test case generation apparatus comprising:
(Appendix 22)
An acquisition means for acquiring an operator of the highest hierarchy in the hierarchy based on a combination of the operator in the logical expression related to the determination and the operation target expression;
Means for applying the acquisition means and the analysis means to a logical expression below an arithmetic expression to be combined by an operator of the highest hierarchy in the hierarchy using a truth value of the arithmetic expression satisfying the predetermined relationship When,
The test case generation device according to attachment 21, further comprising:
(Appendix 23)
23. The test case generation device according to appendix 21 or appendix 22, wherein the selection means determines satisfiability based on whether or not there is a contradiction in the combination relationship between the first sub-expression and the second sub-expression included in the calculation target formula.
(Appendix 24)
In the hierarchy, when a plurality of operators are included in the logical expression, the operator having the higher priority and the operation target expression operated by the operator are arranged in the order of priority of the operation order by the operator. In the calculation target expression that is a hierarchy, and in the priority order of the calculation order by the operator, the next operator and the next calculation target expression calculated by the next operator are 24. The test case generation device according to appendix 22 or appendix 23, which is formed by positioning the next hierarchy immediately below the higher hierarchy as a hierarchy.
(Appendix 25)
The analysis means does not affect the truth value of the result of the operation by the first operator included in the first hierarchy, and the truth value of the first operation target expression to be operated by the first operator Is a common value, and there are a plurality of combinations of true / false values of the operation target expression in the second hierarchy immediately below the first hierarchy, a selection operation capable of selecting the plurality of true / false combinations. 25. The test case generation device according to attachment 24, further comprising means for selecting a combination of true and false values combined by a child and combined by the selection operator.
(Appendix 26)
The analysis means affects a truth value of a calculation result by a first operator included in a first hierarchy, and a truth value of a first calculation target expression to be calculated by the first operator. When there are a plurality of combinations of true / false values of the operation target expression of the second hierarchy immediately below the first hierarchy, all combinations among the plurality of combinations of true / false values The test case generation device according to appendix 24, including means for selecting.
(Appendix 27)
When the operator of the first hierarchy is a product operator, the analysis means is configured such that a combination of true / false values of two first operation target expressions to be operated by the product operator is true, false, false Supplementary Note 24 to Supplementary Note 26, including means for selecting a combination of true / false values in the second calculation target expression immediately below the first hierarchy so as to be true and true or true The test case generator described in 1.
(Appendix 28)
When the first hierarchy operator is a sum operator, the analyzing means determines that a combination of true and false values of two first operation target expressions to be operated by the sum operator is true, false, false The supplementary note according to any one of supplementary notes 24 to 26, including means for selecting a combination of true and false values in the second calculation target expression immediately below the first hierarchy so as to be true and false or false and false The test case generator described in 1.
(Appendix 29)
When the operator of the first hierarchy is a product operator, in the combination of true and false or false and true among the two first operation target expressions to be operated by the product operator, 28. The test case generation device according to appendix 27, wherein operation target expressions for which a truth value is true are combined by a selection operator as selectable operation target expressions.
(Appendix 30)
When the operator of the first hierarchy is a sum operator, in the combination of true and false or false and true among the two first computation target expressions to be computed by the sum operator, 29. The test case generation device according to appendix 28, wherein a calculation target expression whose truth value is false is combined with a selection operator as a selectable calculation target expression.

10 Information processing apparatus 11 CPU
12 Main storage unit 13 Auxiliary storage unit 14 Input unit 15 Output unit 16 Communication unit 101 Judgment expression analysis unit 101a Binary tree conversion unit 102 Truth value rule application unit 103 Test case generation unit 103a Selection analysis unit 104 Satisfiability determination unit 201 Boolean value rule DB
202 Judgment binary tree DB
203 Conditional Expression Map Set DB
204 Test case conditional expression DB
205 test case DB
Tb1 AND rule truth table Tb2 OR rule truth table Tb3 Test case conditional expression table Tb4 Test case table

Claims (12)

On the computer,
Identifies an operation target expression that satisfies a predetermined relationship between the true / false value of the operation result by the operator and the truth value of the operation target expression to be operated by the operator, and specifies a plurality of operation target expressions having the predetermined relationship An analysis step for holding a plurality of operation target expressions specified and obtaining a true / false value of the operation object expression;
A selection step of determining the satisfiability of the calculation target expression for the plurality of calculation target expressions in the predetermined relationship, and selecting the calculation target expression determined to be satisfiable;
Test case generation program to execute An acquisition step of acquiring an operator of the highest hierarchy in the hierarchy based on a combination of the operator in the logical expression related to the determination and the operation target expression;
Applying the obtaining step and the analyzing step to a logical expression below the operation target expression that is combined by an operator of the highest hierarchy in the hierarchy using a truth value of the operation expression that satisfies the predetermined relationship When,
The test case generation program according to claim 1, further executing:   3. The test case generation according to claim 1, wherein the selection step determines satisfiability based on whether or not there is a contradiction in the combination relation between the first sub-expression and the second sub-expression included in the operation target expression. program.   In the hierarchy, when a plurality of operators are included in the logical expression, the operator having the higher priority and the operation target expression operated by the operator are arranged in the order of priority of the operation order by the operator. In the calculation target expression that is a hierarchy, and in the priority order of the calculation order by the operator, the next operator and the next calculation target expression calculated by the next operator are The test case generation program according to claim 2, wherein the test case generation program is formed by positioning the next hierarchy immediately below the higher hierarchy as a hierarchy.   The analysis step does not affect a truth value of an operation result by a first operator included in a first hierarchy, and a truth value of a first operation target expression that is an object of operation by the first operator Is a common value, and there are a plurality of combinations of true / false values of the operation target expression in the second hierarchy immediately below the first hierarchy, a selection operation capable of selecting the plurality of true / false combinations. The test generation case program according to claim 4, further comprising a step of selecting a combination of true and false values combined by a child and combined by the selection operator.   The analyzing step affects a true / false value of a calculation result by a first operator included in a first hierarchy, and a true / false value of a first calculation target expression to be calculated by the first operator. When there are a plurality of combinations of true / false values of the operation target expression of the second hierarchy immediately below the first hierarchy, all combinations among the plurality of combinations of true / false values The test case generation program according to claim 4, comprising a step of selecting.   In the analyzing step, when the operator of the first hierarchy is a product operator, a combination of true / false values of two first operation target expressions to be operated by the product operator is true, false, false 7. The method according to claim 4, further comprising: selecting a combination of true and false values in the second calculation target expression immediately below the first hierarchy so as to be true and true or true and true. Test case generation program described in the section. In the analyzing step, when the operator of the first hierarchy is a sum operator, the combination of the true / false values of the two first operation target expressions to be operated by the sum operator is true, false, false 7. The method according to claim 4, further comprising: selecting a combination of true / false values in the second calculation target expression immediately below the first hierarchy so as to be true and false, or false and false. Test case generation program described in the section.   When the operator of the first hierarchy is a product operator, in the combination of true and false or false and true among the two first operation target expressions to be operated by the product operator, The test case generation program according to claim 7, wherein operation target expressions having true / false values are combined by a selection operator as selectable operation target expressions.   When the operator of the first hierarchy is a sum operator, in the combination of true and false or false and true among the two first computation target expressions to be computed by the sum operator, The test case generation program according to claim 8, wherein a calculation target expression whose truth value is false is combined by a selection operator as a selectable calculation target expression. A method for generating a test case using a computer,
A processor of the computer,
Identifies an operation target expression that satisfies a predetermined relationship between the true / false value of the operation result by the operator and the truth value of the operation target expression to be operated by the operator, and specifies a plurality of operation target expressions having the predetermined relationship In the case where the calculation target expression is retained, a truth value of the calculation target expression is obtained,
Determining satisfiability of the calculation target expression for the plurality of calculation target expressions in the predetermined relationship, and selecting the calculation target expression determined to be satisfiable;
Test case generation method. Identifies an operation target expression that satisfies a predetermined relationship between the true / false value of the operation result by the operator and the truth value of the operation target expression to be operated by the operator, and specifies a plurality of operation target expressions having the predetermined relationship An analysis means for holding a plurality of specified calculation target expressions and obtaining a true / false value of the calculation target expression,
Selection means for determining satisfiability of the calculation target expression for the plurality of calculation target expressions having the predetermined relationship, and selecting the calculation target expression determined to be satisfiable;
A test case generation apparatus comprising:

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