JP6205965B2 - Test data generation program, test data generation method, and test data generation apparatus - Google Patents

Description

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

  Conventionally, in program development, there is a technique for testing a program operation using a test case. The test case has the value of the input variable that is given to the program. In addition, there is a technique for performing a regression test for determining whether a problem has occurred in a program after the change due to the change of the program when the program is changed. In the regression test, for example, the same test case is used to test the operation of the program before and after the change, and whether or not a defect has occurred based on whether the test results of the program before and after the change match. Determine.

  As related technologies, for example, the test source code can be analyzed to reflect changes in the input / output interface, and input relationship information including input values and output values or changed input / output relationship information can be created. , Create test cases or modified test cases. Also, for example, in the test data created from the program data before the change, the part related to the function that has been changed due to the program change is rewritten, and the part that has not been changed is diverted and the test for the program after the change is made There is technology to create data. In addition, for example, a technology that statically and mechanically calculates the locations that are considered valid and invalid in the program, supports the analysis of the program by reporting the survey results, and discards invalid instructions There is.

JP 2009-205239 A JP 2011-159008 A JP-A-10-63536

  However, in the conventional technology, when a variable used in the program is added, deleted, or renamed when the program is changed, the test case used for testing the program before the change when testing the changed program May not be used as is.

  In one aspect, an object of the present invention is to provide a test data generation program, a test data generation method, and a test data generation apparatus that can generate test data used for a regression test of a program after change.

  According to one aspect of the present invention, a variable used in the method in the first program in which a method to be tested is defined, and a method in the second program in which the content of the method is changed from the first program are used. A change pattern of a variable used for the method is specified based on a comparison result of comparing with a variable to be changed, and a variable used for the method in the first program is changed based on the specified change pattern. A test data generation program, a test data generation method, and a test data generation device that generate a program and generate test data by symbolically executing the generated third program are proposed.

  According to one aspect of the present invention, it is possible to generate test data used for a regression test of a program after change.

FIG. 1 is an explanatory diagram of an operation example of the test data generation device 100 according to the embodiment. FIG. 2 is a block diagram illustrating a hardware configuration example of the test data generation apparatus 100. FIG. 3 is a block diagram illustrating a functional configuration example of the test data generation apparatus 100. FIG. 4 is an explanatory diagram showing an example of program change contents. FIG. 5 is an explanatory diagram (part 1) illustrating an example of the difference information specifying process. FIG. 6 is an explanatory diagram (part 2) of an example of the difference information specifying process. FIG. 7 is an explanatory diagram (part 3) of an example of the difference information specifying process. FIG. 8 is an explanatory diagram (part 4) of an example of the difference information specifying process. FIG. 9 is an explanatory diagram (part 5) of an example of the difference information specifying process. FIG. 10 is an explanatory diagram (part 6) of an example of the difference information specifying process. FIG. 11 is an explanatory diagram (part 7) of an example of the difference information specifying process. FIG. 12 is an explanatory diagram (part 8) of an example of the difference information specifying process. FIG. 13 is an explanatory diagram (No. 9) illustrating an example of the difference information specifying process. FIG. 14 is an explanatory diagram showing an example of processing of a program. FIG. 15 is an explanatory diagram (part 1) of an example of the test data generation process. FIG. 16 is an explanatory diagram (part 2) of an example of the test data generation process. FIG. 17 is an explanatory diagram (part 1) illustrating an example of the refactoring process. FIG. 18 is an explanatory diagram (part 2) of an example of the refactoring process. FIG. 19 is an explanatory diagram (part 3) of an example of the refactoring process. FIG. 20 is an explanatory diagram (part 4) of an example of the refactoring process. FIG. 21 is an explanatory diagram (part 5) of an example of the refactoring process. FIG. 22 is an explanatory diagram showing an example of the regression test. FIG. 23 is a flowchart illustrating an example of a procedure for executing a regression test. FIG. 24 is a flowchart illustrating an example of the difference information identification processing procedure. FIG. 25 is a flowchart illustrating an example of a processing procedure of a program. FIG. 26 is a flowchart illustrating an example of a test data generation processing procedure. FIG. 27 is a flowchart illustrating an example of a refactoring processing procedure.

  Exemplary embodiments of a test data generation program, a test data generation method, and a test data generation apparatus according to the present invention will be explained below in detail with reference to the accompanying drawings.

(Operation example of test data generation apparatus 100)
FIG. 1 is an explanatory diagram of an operation example of the test data generation device 100 according to the embodiment.

  The test data generation device 100 is a computer that generates a test case of the second program P2 in which the contents of the method of the first program P1 in which the test target method is defined. In the following description, the first program may be referred to as “pre-change program P1”. In the following description, the second program may be referred to as “changed program P2”.

  Changing the contents of a method includes changing a variable used in the method. Here, the variable includes a variable declared as an argument (method parameter) of a method included in the program and a variable declared in the program as a parameter other than the method argument. The variable is a so-called program interface. In the following description, a variable declared as an argument of a method included in a program may be referred to as a “parameter variable”. In the following description, a variable declared in a program may be referred to as a “program variable”.

  (1) The test data generation device 100 specifies difference information indicating a change pattern of a variable used for a test target method based on the pre-change program P1 and the post-change program P2. Details of the difference information specifying process will be described later with reference to FIGS.

  (2) The test data generation apparatus 100 processes the pre-change program P1 based on the difference information, and creates a program variation for creating test data of the post-change program P2. Details of the program processing will be described later with reference to FIG. Thereby, the test data generation device 100 can create a program variation that can correspond to the changed program P2.

  (3) The test data generation device 100 generates a plurality of test data by executing symbolically the post-processing program. Symbolic execution is also called symbol execution. Details of the test data generation process will be described later with reference to FIGS. 15 and 16. Here, the test data includes a pair of the test case TC1 and the execution result R1. The test case TC1 is a value of an input variable that becomes a program execution condition. The execution result R1 is the value of the output variable that becomes the execution result of the program. As a result, the test data generating apparatus 100 can generate a plurality of test data that can be used for the regression test on the post-change program P2.

  (4) The test data generation device 100 has a pair of the test case TC1 * and the execution result R1 * after integration by refactoring a plurality of test data, integrating a plurality of pairs having overlapping test case portions. Generate new test data. Details of the refactoring process will be described later with reference to FIGS. As a result, the test data generating apparatus 100 can generate a plurality of test data and generate test data used for the regression test even for the changed program P2 including the change of the program variable or the parameter variable.

  For this reason, for example, compared with a case where the execution person of the regression test refers to the program P1 before change and the program P2 after change and creates test data for the program P2 after change, the work of the person who executes the regression test The amount can be reduced. Further, it is possible to prevent erroneous test data from being created by a regression tester.

  (5) The test data generation device 100 may further execute a regression test using the retested new test data. The regression test is also called a regression test. For example, in the regression test, the test data generation device 100 compares the execution result R1 * corresponding to the test case TC1 * with the execution result R2 when the changed program P2 is executed using the test case TC1 * as an execution condition. . The test data generation device 100 may determine whether or not the changed program P2 has a problem based on the comparison result. Details of the regression test will be described later with reference to FIG.

  Thereby, the test data generation device 100 can execute the regression test on the program P2 after the change in which the program variable or the parameter variable is changed, and can output the test result of the regression test. Then, the user of the test data generation device 100 can grasp whether or not there is a problem in the changed program P2 from the test result of the regression test.

(Example of hardware configuration of test data generation apparatus 100)
FIG. 2 is a block diagram illustrating a hardware configuration example of the test data generation apparatus 100. In FIG. 2, a test data generation apparatus 100 includes a CPU (Central Processing Unit) 201, a ROM (Read Only Memory) 202, a RAM (Random Access Memory) 203, a magnetic disk drive (Hard Disk Drive) 204, and a magnetic disk. A disk 205, an optical disk drive 206, an optical disk 207, a display 208, an interface (I / F: Interface) 209, a keyboard 210, a mouse 211, a scanner 212, and a printer 213 are provided. Each component is connected by a bus 200.

  Here, the CPU 201 governs overall control of the test data generation apparatus 100. The ROM 202 stores a program such as a boot program. The RAM 203 is used as a work area for the CPU 201. The magnetic disk drive 204 controls reading / writing of data with respect to the magnetic disk 205 according to the control of the CPU 201. The magnetic disk 205 stores data written under the control of the magnetic disk drive 204.

  The optical disk drive 206 controls reading / writing of data with respect to the optical disk 207 according to the control of the CPU 201. The optical disk 207 stores data written under the control of the optical disk drive 206, or causes the computer to read data stored on the optical disk 207.

  The display 208 displays data such as a document, an image, and function information as well as a cursor, an icon, or a tool box. As this display 208, for example, a liquid crystal display, a plasma display, or the like can be adopted.

  The I / F 209 is connected to a network 214 such as a LAN (Local Area Network), a WAN (Wide Area Network), and the Internet through a communication line, and is connected to other devices via the network 214. The I / F 209 controls an internal interface with the network 214 and controls data input / output from an external device. For example, a modem or a LAN adapter may be employed as the I / F 209.

  The keyboard 210 includes keys for inputting characters, numbers, various instructions, and the like, and inputs data. Moreover, a touch panel type input pad or a numeric keypad may be used. The mouse 211 performs cursor movement, range selection, window movement, size change, and the like. A trackball or a joystick may be used as long as they have the same function as a pointing device.

  The scanner 212 optically reads an image and takes in the image data into the test data generation apparatus 100. The scanner 212 may have an OCR (Optical Character Reader) function. The printer 213 prints image data and document data. As the printer 213, for example, a laser printer or an ink jet printer can be adopted. Further, at least one of the optical disk drive 206, the optical disk 207, the display 208, the keyboard 210, the mouse 211, the scanner 212, and the printer 213 may be omitted.

(Functional configuration example of test data generation apparatus 100)
Next, a functional configuration example of the test data generation apparatus 100 will be described with reference to FIG.

  FIG. 3 is a block diagram illustrating a functional configuration example of the test data generation apparatus 100. The test data generation device 100 includes a specifying unit 301, a creation unit 302, a generation unit 303, and a determination unit 304. For example, the specifying unit 301, the creating unit 302, the generating unit 303, and the determining unit 304 store programs stored in a storage device such as the ROM 202, the RAM 203, the magnetic disk 205, and the optical disk 207 illustrated in FIG. The function is realized by executing or by the I / F 209.

  The specifying unit 301 compares a variable used for the method in the first program in which the test target method is defined with a variable used in the method in the second program in which the content of the method is changed from the first program. Next, the specifying unit 301 specifies a change pattern of a variable used for the method based on the comparison result. Here, the first program is, for example, the pre-change program P1 described above. The second program is, for example, the post-change program P2 described above. The variable used for the method is a parameter variable that becomes an argument of the method or a program variable other than the argument of the method.

  For example, the identifying unit 301 identifies at least one of the first to third modification patterns for the variable used in the method. The first change pattern is a change pattern that is changed to another variable of the same format after the content of the method is changed. The second change pattern is a change pattern that is deleted after changing the contents of the method. The third change pattern is a change pattern that is added after changing the contents of the method.

  Here, the program variables in the pre-change program P1 are “x, b1” and the parameter variables are “a, b”, the program variables in the post-change program P2 are “i, b2”, and the parameter variables are “a, c”. Take the case as an example.

  In this case, the specifying unit 301 uses {x → i, x → φ}, {b1 → b2, b1 → φ}, {b → c, b → φ}, {φ → i, Specify φ → b2, φ → c}. Here, φ is a dummy variable. The above-mentioned {x → i} represents a change pattern changed from x to i. The above-mentioned {x → φ} represents a change pattern in which x is deleted. The above-mentioned {φ → i} represents a change pattern in which i is added.

  Thereby, the specific | specification part 301 can specify the change pattern which can be taken. The identified data is stored in a storage area such as the RAM 203, the magnetic disk 205, and the optical disk 207, for example.

  The creation unit 302 creates a third program in which variables used for the method in the first program are changed based on the specified change pattern. The third program is a test data generation program for the second program. The third program is, for example, the program variation described above.

  For example, the creation unit 302 creates the third program based on a combination of change patterns specified for each variable used in the method. Specifically, the creating unit 302 creates the third program based on a combination of change patterns for different variables used in the method.

  Here, by the specifying unit 301, as change patterns, {x → i, x → φ}, {b1 → b2, b1 → φ}, {b → c, b → φ}, {φ → i, A case where φ → b2, φ → c} is specified will be described as an example.

  In this case, based on the combination of {x → i}, {b1 → b2}, and {b → c}, the creation unit 302 sets x of the pre-change program P1 to i, b1 to b2, and b Replace c with c to create a program variation. Further, the creation unit 302 replaces x in the pre-change program P1 with x_r based on a combination of {x → φ}, {b1 → b2}, {b → c}, and {φ → i}. Then, b1 is replaced with b2, b is replaced with c, and i is added to create a program variation. Here, x_r is a variable for deletion indicating that x is deleted.

  Thereby, the creation unit 302 can create a program variation for generating test data of the changed program P2. The created data is stored in a storage area such as the RAM 203, the magnetic disk 205, and the optical disk 207, for example.

  The generating unit 303 generates test data by symbolically executing the generated third program. Here, symbolic execution is called symbol execution. Since the test data generation by symbolic execution is a conventional technique, the description is omitted.

  The generation unit 303 generates, for example, test data having an input variable value that becomes an execution condition of a sequence of statements in the created third program and an output variable value that becomes an execution result in association with each other. Specifically, the generation unit 303 includes a test having a first pair in which the execution condition “i = 0, b2 = false, a = 0, c = 0” and the execution result “{0, 1}” are associated with each other. Generate data.

  In this case, when the pre-change program P1 is executed with the execution condition “i = 0, b2 = false, a = 0, c = 0” as input variables, the first pair has an execution result “{0, 1}”. "" Indicates that the value of the output variable. Further, the first pair has an execution result “{0, 1}” when the changed program P2 is executed with the execution condition “i = 0, b2 = false, a = 0, c = 0” as input variables. Indicates that one of the values is the expected value of the output variable.

  The test data may further include a second pair in which the execution condition “i = 0, b2 = true, a = −1, c = 0” and the execution result “0” are associated with each other. Further, the test data may further include a third pair in which the execution condition “i = 0, b2 = false, a = −1, c = 0” and the execution result “0” are associated with each other.

  Thereby, the production | generation part 303 can produce | generate the test data of the program P2 after a change. The generated data is stored in a storage area such as the RAM 203, the magnetic disk 205, and the optical disk 207, for example.

The determination unit 304 determines whether or not the value of the output variable when the second program is executed using the value of the input variable included in the generated test data as an execution condition matches the value of the output variable included in the test data. To do. For example, when the changed program P2 is executed with the execution condition “i = 0, b2 = true, a = −1, c = 0” as input variables, the determination unit 304 has an execution result of “−1”. Then, it is determined whether or not it matches the execution result “0” that is the expected value of the output variable. Here, when the execution result when a certain execution condition is an input variable does not match the execution result corresponding to the execution condition included in the test data, the determination unit 304 changes the method function to change the function of the method. It may be determined that has been changed.

The determination unit 304 determines whether or not the value of the output variable when the second program is executed using the value of the input variable included in the generated test data as an execution condition is included in the value of the output variable included in the test data. To do. For example, the determination unit 304 outputs “−1” as the execution result when the changed program P2 is executed with the execution condition “i = 0, b2 = false, a = 0, c = 0” as input variables. It is determined whether or not it matches any value of the execution result “{0, 1}” that is the expected value of the variable. Here, when the execution result when an execution condition is an input variable is not included in the execution result corresponding to the execution condition included in the test data, the determination unit 304 changes the method content to change the method. It may be determined that the function has been changed.

The determination unit 304 has the same input variable value that is not renamed before and after the change of the method among the input variables that become the execution condition in the pair that associates the execution condition and the execution result of the generated test data. And pairs that have the same execution result. Then, the determination unit 304 sets the value of the output variable when the second program is executed using the value of the input variable included in any of the grouped pairs as an execution condition to the pair. It is determined whether it is included in the value of the included output variable.

  The determination unit 304, for example, includes a pair in which the execution condition “i = 0, b2 = true, a = −1, c = 0” and the execution result “0” are associated with each other, and the execution condition “i = 0, b2 = false”. , A = −1, c = 0 ”and a pair in which the execution result“ 0 ”is associated with each other. Next, the determination unit 304 determines, for each execution condition, whether or not the execution result when the changed program P2 is executed with the execution condition as an input variable is included in the execution result corresponding to the execution condition. . Here, the determination unit 304 includes a pair in which an execution result when an execution condition is an input variable is included in the execution result corresponding to the execution condition included in the test data in the grouped pair. It may be determined whether or not there is. Then, when there is no pair, the determination unit 304 may determine that the function of the method has been changed by changing the content of the method.

  Thereby, the determination unit 304 can execute the regression test. The determination result is stored in a storage area such as the RAM 203, the magnetic disk 205, and the optical disk 207, for example. The determination unit 304 may output a determination result. Examples of the output format include display on the display 208, print output to the printer 213, and transmission to an external device by the I / F 209. Alternatively, the data may be stored in a storage area such as the RAM 203, the magnetic disk 205, and the optical disk 207. Thereby, the user of the test data generation device 100 can determine from the determination result whether or not the function of the method has been changed due to the change in the content of the method.

(Example of program changes)
Next, an example of program change contents will be described with reference to FIG.

  FIG. 4 is an explanatory diagram showing an example of program change contents. In the example of FIG. 4, an int type program variable “x” and a Boolean type program variable “b1” are declared in the pre-change program P1. The pre-change program P1 defines a max method, and declares an int type parameter variable “a” and an int type parameter variable “b” as arguments of the max method.

  On the other hand, the changed program P2 does not have the int type program variable “x” and the Boolean type program variable “b1”, but the int type program variable “i” and the Boolean type program variable “b2”. And are declared. In the changed program P2, an int type parameter variable “a” and an int type parameter variable “c” are declared as arguments of the max method.

(An example of difference information identification processing)
Next, an example of the difference information specifying process will be described with reference to FIGS.

  5 to 13 are explanatory diagrams illustrating an example of the difference information specifying process. As shown in FIG. 5, the test data generation device 100 extracts a set of program variables V1 = {x, b1} and a set of parameter variables P1 = {a, b} from the pre-change program P1. In the following description, the extracted set of program variables V1 and the set of parameter variables P1 may be collectively referred to as “pre-change variable data 500”. Next, the description shifts to the description of FIG.

  As illustrated in FIG. 6, the test data generation apparatus 100 includes a post-change program P2 including a set of program variables V2 = {i, b2} and a set of parameter variables P2 = {a, c}. Extract variable data. In the following description, the extracted set of program variables V2 and the set of parameter variables P2 may be collectively referred to as “changed variable data 600”. Next, the description proceeds to FIG.

  As illustrated in FIG. 7, the test data generation apparatus 100 extracts a set of invariant-named program variables V_INV = V2∩V1 = {} based on pre-change variable data 500 and post-change variable data 600. Further, the test data generating apparatus 100 extracts a set of parameter variables P_INV = P2１P1 = {a} having invariant names based on the pre-change variable data 500 and the post-change variable data 600. In the following description, the extracted invariant name program variable set V_INV and the invariant name parameter variable set P_INV may be collectively referred to as “invariant variable data 701”.

  Further, the test data generation apparatus 100 extracts a set of additional candidate program variables V_ADD = V2-V1 = {i, b2} based on the pre-change variable data 500 and the post-change variable data 600. Further, the test data generation apparatus 100 extracts a set of additional candidate parameter variables P_ADD = P2-P1 = {c} based on the pre-change variable data 500 and the post-change variable data 600. In the following description, the set of additional candidate program variables V_ADD and the set of additional candidate parameter variables P_ADD may be collectively referred to as “additional candidate variable data 702”.

  Further, the test data generation apparatus 100 extracts a set V_REM = V1-V2 = {x, b1} of deletion candidate program variables based on the pre-change variable data 500 and the post-change variable data 600. Further, the test data generation device 100 extracts a set of parameter variables P_REM = P1-P2 = {b} of deletion candidates based on the pre-change variable data 500 and the post-change variable data 600. In the following description, the extracted deletion candidate program variable set V_REM and the deletion candidate parameter variable set P_REM may be collectively referred to as “deletion candidate variable data 703”. Next, the description proceeds to FIG.

  As illustrated in FIG. 8, the test data generation device 100 adds a dummy variable to the program variable set V_ADD = {i, b2} of the additional candidate variable data 702 and sets the program variable set V_ADD = {i, b2, φ}. Further, the test data generating apparatus 100 adds a dummy variable to the parameter variable set P_ADD = {c} of the additional candidate variable data 702 so as to obtain a parameter variable set P_ADD = {c, φ}.

  Further, the test data generating apparatus 100 adds a dummy variable to the program variable set V_REM = {x, b1} of the deletion candidate variable data 703 to make a program variable set V_REM = {x, b1, φ}. Further, the test data generating apparatus 100 adds a dummy variable to the parameter variable set P_REM = {b} of the deletion candidate variable data 703 to make the parameter variable set P_REM = {b, φ}.

  Then, the test data generation device 100 combines the addition candidate variable data 702 to which the dummy variable is added and the deletion candidate variable data 703 to which the dummy variable is added. In the following description, the addition candidate variable data 702 to which dummy variables are added and the deletion candidate variable data 703 to which dummy variables are added may be collectively referred to as “combined data 800”. Next, the description shifts to the description of FIG.

  As shown in FIG. 9, the test data generation apparatus 100 uses the program data change pattern V_PATTERN = V_REM → V_ADD = {x → i, x → b2, x → φ, b1 → i, b1 → b2, b1 → φ, φ → i, φ → b2, φ → φ} are specified. Further, the test data generation apparatus 100 specifies a parameter variable change pattern P_PATTERN = P_REM → P_ADD = {b → c, b → φ, φ → c, φ → φ} based on the combined data 800. In the following description, the program variable change pattern V_PATTERN and the parameter variable change pattern P_PATTERN may be collectively referred to as “combination data 901”.

  Next, the test data generating apparatus 100 removes the φ → φ pair and the pair of different types from the program variable change pattern V_PATTERN in the combination data 901, and the program variable change pattern V_PATTERN = {x → i, x → φ, b1 → b2, b1 → φ, φ → i, φ → b2}. Further, the test data generating apparatus 100 removes the φ → φ pair and the pair of different types from the parameter variable change pattern P_PATTERN in the combination data 901, and changes the parameter variable change pattern P_PATTERN = {b → c , B → φ, φ → c}.

  In the following description, the program variable change pattern V_PATTERN from which the pair has been removed and the parameter variable change pattern P_PATTERN from which the pair has been removed may be collectively referred to as “post-removal data 902”. Next, the description proceeds to FIG.

  As shown in FIG. 10, the test data generation apparatus 100, based on the post-removal data 902, changes pattern V_x = {x → i, x → φ} for the program variables and parameter variables of the pre-change program P1; V_b1 = {b1 → b2, b1 → φ} and M_b = {b → c, b → φ} are extracted. Further, the test data generation apparatus 100 extracts an additional pattern φ = {φ → i, φ → b2, φ → c} for the program variable and parameter variable of the changed program P2 based on the post-removal data 900. . In the following description, the extracted change pattern V_x, V_b1, M_b, and the additional pattern φ may be collectively referred to as “change pattern data 1000”. Next, the description proceeds to FIG.

  As shown in FIG. 11, the test data generation device 100, based on the change pattern data 1000, changes variation VARIATION = V_x → V_b1 → M_b = {{x → i, b1 → b2, b → c}, {x → i, b1 → b2, b → φ}, {x → i, b1 → φ, b → c}, {x → i, b1 → φ, b → φ}, {x → φ, b1 → b2, b → c}, {x → φ, b1 → b2, b → φ}, {x → φ, b1 → φ, b → c}, {x → φ, b1 → φ, b → φ}}. In the following description, the extracted change variation may be referred to as “variation data 1100”. Next, the description proceeds to FIG.

  As shown in FIG. 12, the test data generation device 100 adds the change pattern φ for the program variable and the parameter variable of the changed program P2 to the variation data 1100, and changes variation VARIATION = V_x → V_b1 → M_b = {{X → i, b1 → b2, b → c}, {x → i, b1 → b2, b → φ, φ → c}, {x → i, b1 → φ, b → c, φ → b2} , {X → i, b1 → φ, b → φ, φ → b2, φ → c}, {x → φ, b1 → b2, b → c, φ → i}, {x → φ, b1 → b2, b → φ, φ → i, φ → c}, {x → φ, b1 → φ, b → c, φ → i, φ → b2}, {x → φ, b1 → φ, b → φ, φ → i, φ → b2, φ → c}}. In the following description, the added change variation may be referred to as “additional variation data 1200”. Next, the description proceeds to FIG.

  As illustrated in FIG. 13, the test data generation device 100 specifies the program change content from the change variation. In the example of FIG. 13, the test data generation apparatus 100 starts from the change variation B1 “{x → i, b1 → b2, b → c}”, and the program change content “x is i, b1 is b2, and b is “Name change to c” is specified.

  Further, the test data generating apparatus 100 changes the program change contents “x to i, b1 to b2 from the change variation B2“ {x → i, b1 → b2, b → φ, φ → c} ”, “Delete b, add c” is specified.

  Further, the test data generation apparatus 100 changes the program change contents “x to i, b to c from the change variation B3“ {x → i, b1 → φ, b → c, φ → b2} ”, “Delete b1, add b2” is specified.

  Further, the test data generating apparatus 100 changes the program change contents “x to i, b1” from the change variation B4 “{x → i, b1 → φ, b → φ, φ → b2, φ → c}”. “Delete b, add b2 and c”.

  Further, the test data generation device 100 deletes the program change contents “x is deleted, i is added, and b1 is changed to b2 from the change variation B5“ {x → φ, b1 → b2, b → c, φ → i} ”. , B is renamed to c ”.

  Further, the test data generation device 100 changes the program change content “b1 to b2” from the change variation B6 “{x → φ, b1 → b2, b → φ, φ → i, φ → c}”, x Specify “Delete b, add i and c”.

  Further, the test data generating apparatus 100 changes the program change content “b to c, x from the change variation B7“ {x → φ, b1 → φ, b → c, φ → i, φ → b2} ”, x “Delete b1 and add i · b2”.

  In addition, the test data generation apparatus 100 reads the program change contents “x · b1 · from the change variation B8“ {x → φ, b1 → φ, b → φ, φ → i, φ → b2, φ → c} ”. “Delete b, add i · b2 · c” ”is specified.

(Example of program processing)
Next, an example of program processing will be described with reference to FIG.

  FIG. 14 is an explanatory diagram showing an example of processing of a program. As shown in FIG. 14, the test data generation device 100 processes the pre-change program P1 according to the change content specified from the change variation B, and program variations P1-1 to P1-8 corresponding to the change variations B1 to B8. Create

  In the example of FIG. 14, the test data generation device 100 replaces x in the pre-change program P1 with i, b1 with b2, and b with c according to the change variation B1, and the program variation P1 corresponding to the change variation B1. -1.

  Further, the test data generation device 100 processes the pre-change program P1 according to the change variation B2. For example, the test data generation apparatus 100 replaces x in the pre-change program P1 with i, replaces b1 with b2, replaces b with b_r indicating deletion, adds c, and corresponds to the change variation B2 A variation P1-2 is created.

  Further, the test data generation device 100 processes the pre-change program P1 according to the change variation B3. For example, the test data generating apparatus 100 replaces x in the pre-change program P1 with i, replaces b with c, replaces b1 with b1_r indicating deletion, adds b2, and corresponds to the change variation B3. A variation P1-3 is created.

  Further, the test data generation device 100 processes the pre-change program P1 according to the change variation B4. For example, the test data generation device 100 replaces x in the pre-change program P1 with i, replaces b1 · b with b1_r · b_r indicating that b1 · b is deleted, adds b2 · c, and corresponds to the change variation B4. A program variation P1-4 is created.

  Further, the test data generation device 100 processes the pre-change program P1 according to the change variation B5. For example, the test data generating apparatus 100 replaces x in the pre-change program P1 with x_r indicating that it has been deleted, adds i, replaces b1 with b2, replaces b with c, and corresponds to the change variation B5. Variation P1-5 is created.

  Moreover, the test data generation device 100 processes the pre-change program P1 according to the change variation B6. For example, the test data generating apparatus 100 replaces b1 in the pre-change program P1 with b2, replaces x · b with x_r · b_r indicating deletion, adds i · c, and corresponds to the change variation B6. Program variation P1-6 is created.

  Moreover, the test data generation device 100 processes the pre-change program P1 according to the change variation B7. For example, the test data generation device 100 replaces b in the pre-change program P1 with c, replaces x · b1 with x_r · b1_r indicating deletion, adds i · b2, and corresponds to the change variation B7. Program variation P1-7 is created.

  Further, the test data generation device 100 processes the pre-change program P1 according to the change variation B8. The test data generation device 100 replaces x · b1 · r · b_r indicating that x · b1 · b in the pre-change program P1 is deleted, adds i · b2 · c, and the program corresponding to the change variation B8. Variation P1-8 is created.

(Example of test data generation process)
Next, an example of test data generation processing will be described with reference to FIGS. 15 and 16.

  15 and 16 are explanatory diagrams illustrating an example of the test data generation process. As shown in FIGS. 15 and 16, the test data generation apparatus 100 generates test data by performing symbolic execution of the created program variations P1-1 to P1-8. The test data includes the value of an input variable that becomes a test case and the value of an output variable that becomes an execution result in a sequence of statements obtained by symbolic execution. Since generation of test data by symbolic execution is a conventional technique, a detailed description thereof will be omitted.

  In the example of FIG. 15, the test data generation device 100 generates test data T1 from a series of command statements obtained by symbolically executing the program variation P1-1. Here, the test data T1 has a pair in which the test case “i = 0, b2 = false, a = 0, c = 0” and the execution result “0” are associated with each other. The test data T1 has a pair in which the test case “i = 0, b2 = true, a = −1, c = 0” and the execution result “0” are associated with each other. The test data T1 has a pair in which the test case “i = 0, b2 = false, a = 0, c = 1” and the execution result “−1” are associated with each other.

  In addition, the test data generation device 100 generates test data T2 from a series of command statements obtained by symbolically executing the program variation P1-2. Here, the test data T2 has a pair in which the test case “i = 0, b2 = false, a = 0, b_r = 0, c = 0” and the execution result “0” are associated with each other. The test data T2 has a pair in which the test case “i = 0, b2 = true, a = −1, b_r = 0, c = 0” and the execution result “0” are associated with each other. The test data T2 has a pair in which the test case “i = 0, b2 = false, a = 0, b_r = 1, c = 0” and the execution result “−1” are associated with each other.

  In addition, the test data generation apparatus 100 generates test data T3 from a series of command statements obtained by symbolically executing the program variation P1-3. Here, the test data T3 has a pair in which the test case “i = 0, b1_r = false, a = 0, c = 0, b2 = false” and the execution result “0” are associated with each other. The test data T3 has a pair in which the test case “i = 0, b1_r = true, a = −1, c = 0, b2 = false” and the execution result “0” are associated with each other. The test data T3 includes a pair in which the test case “i = 0, b1_r = false, a = 0, c = 1, b2 = false” and the execution result “−1” are associated with each other.

  In addition, the test data generation device 100 generates test data T4 from a series of command statements obtained by symbolically executing the program variation P1-4. Here, the test data T4 has a pair in which the test case “i = 0, b1_r = false, a = 0, b_r = 0, b2 = false, c = 0” and the execution result “0” are associated with each other. The test data T4 has a pair in which the test case “i = 0, b1_r = true, a = −1, b_r = 0, b2 = false, c = 0” and the execution result “0” are associated with each other. The test data T4 includes a pair in which the test case “i = 0, b1_r = false, a = 0, b_r = 1, b2 = false, c = 0” and the execution result “−1” are associated with each other.

  In the example of FIG. 16, the test data generation device 100 generates test data T5 from a sequence of command statements obtained by symbolically executing the program variation P1-5. Here, the test data T5 has a pair in which the test case “x_r = 0, b2 = false, a = 0, c = 0, i = 0” and the execution result “0” are associated with each other. The test data T5 includes a pair in which the test case “x_r = 0, b2 = true, a = −1, c = 0, i = 0” and the execution result “0” are associated with each other. The test data T5 has a pair in which the test case “x_r = 0, b2 = false, a = 0, c = 1, i = 0” and the execution result “−1” are associated with each other.

  In addition, the test data generation device 100 generates test data T6 from a series of command statements obtained by symbolically executing the program variation P1-6. Here, the test data T6 has a pair in which the test case “x_r = 0, b2 = false, a = 0, b_r = 0, i = 0, c = 0” and the execution result “0” are associated with each other. The test data T6 has a pair in which the test case “x_r = 0, b2 = true, a = −1, b_r = 0, i = 0, c = 0” and the execution result “0” are associated with each other. The test data T6 includes a pair in which the test case “x_r = 0, b2 = false, a = 0, b_r = 1, i = 0, c = 0” and the execution result “−1” are associated with each other.

  In addition, the test data generation device 100 generates test data T7 from a series of command statements obtained by symbolically executing the program variation P1-7. Here, the test data T7 has a pair in which the test case “x_r = 0, b1_r = false, a = 0, c = 0, i = 0, b2 = false” and the execution result “0” are associated with each other. The test data T7 has a pair in which the test case “x_r = 0, b1_r = true, a = −1, c = 0, i = 0, b2 = false” and the execution result “0” are associated with each other. The test data T7 includes a pair in which the test case “x_r = 0, b1_r = false, a = 0, c = 1, i = 0, b2 = false” and the execution result “−1” are associated with each other.

  In addition, the test data generation device 100 generates test data T8 from a series of command statements obtained by symbolically executing the program variation P1-8. Here, the test data T8 is a pair in which the test case “x_r = 0, b1_r = false, a = 0, b_r = 0, i = 0, b2 = false, c = 0” and the execution result “0” are associated with each other. Have The test data T8 is a pair in which the test case “x_r = 0, b1_r = true, a = −1, b_r = 0, i = 0, b2 = false, c = 0” and the execution result “0” are associated with each other. Have The test data T8 includes a pair in which the test case “x_r = 0, b1_r = false, a = 0, b_r = 1, i = 0, b2 = false, c = 0” and the execution result “−1” are associated with each other. Have

(Example of refactoring process)
Next, an example of the refactoring process will be described with reference to FIGS.

  17 to 21 are explanatory diagrams illustrating an example of the refactoring process. As shown in FIG. 17, the test data generation device 100 deletes the deletion candidate program variable item and the deletion candidate parameter variable item from the test cases of the test data T1 to T8.

  In the example of FIG. 17, the test data generation device 100 deletes the item for the parameter variable b_r from the test data T2 corresponding to the program variation P1-2. Further, the test data generating apparatus 100 deletes the item for the program variable b1_r from the test data T3 corresponding to the program variation P1-3.

  In addition, the test data generation device 100 deletes items for the parameter variable b_r and the program variable b1_r from the test data T4 corresponding to the program variation P1-4. Further, the test data generating apparatus 100 deletes the item for the program variable x_r from the test data T5 corresponding to the program variation P1-5.

  In addition, the test data generation device 100 deletes items for the program variable x_r and the parameter variable b_r from the test data T6 corresponding to the program variation P1-6. In addition, the test data generation device 100 deletes items for the program variables x_r and b1_r from the test data T7 corresponding to the program variation P1-7.

  In addition, the test data generation device 100 deletes items for the program variables x_r, b1_r, and the parameter variable b_r from the test data T8 corresponding to the program variation P1-8. Next, the description proceeds to FIG.

  As illustrated in FIG. 18, the test data generation device 100 merges a pair of test cases and execution results included in the test data T1 to T8 to create new test data. In the example of FIG. 18, when merging the test data T1 to T8, the test data generation device 100 determines that if there are overlapping pairs such as a pair surrounded by a solid line or a pair surrounded by a dotted line. The merged test data 1800 is created. Next, the description shifts to the description of FIG.

  As shown in FIG. 19, if there is a pair with duplicate test cases in the merged test data 1800, the test data generation apparatus 100 collects them together and creates new test data. In the example of FIG. 19, the test data generation device 100 collects the pairs of the first row and the fourth row into one and creates the combined test data 1900. Next, the description shifts to the description of FIG.

  As illustrated in FIG. 20, the test data generation device 100 specifies an invariant name from the invariant variable data 701. Next, the test data generation apparatus 100 groups the test data 1900 if there is a pair in which the program variable and the parameter variable of the invariant name in the test case have the same value.

  In the example of FIG. 20, the test data generation device 100 specifies the invariant name “a” from the invariant variable data 701. Next, the test data generation apparatus 100 groups the pairs of the first and third lines in the test data 1900 in which the item of the program variable “a” with the invariant name has the same value. In addition, the test data generation apparatus 100 groups the pairs of the second and fourth lines in the test data 1900 in which the item of the program variable “a” of the invariant name has the same value. In the following description, the grouping result may be expressed as “group data 2000”. Next, the description proceeds to FIG.

  As illustrated in FIG. 21, the test data generation device 100 extracts a group with the same execution result from the grouped groups represented by the group data 2000. In the example of FIG. 21, the test data generation device 100 extracts a pair group of the second and fourth rows whose execution results are both “0” and match. In the following description, data representing the extracted group may be referred to as “identical determination group data 2100”.

(An example of a regression test)
Next, an example of a regression test will be described with reference to FIG.

  FIG. 22 is an explanatory diagram showing an example of the regression test. As shown in FIG. 22, the test data generation device 100 executes a regression test using the test data. Next, the test data generation device 100 compares the execution result of the test data with the execution result of the regression test, and determines whether or not the function of the method has been changed.

  In the example of FIG. 22, the test data generation device 100 executes the regression test of the changed program P2 using the test cases of each pair of the first to fourth lines included in the test data as input variables. The execution result 2202 “0, 0, −1, −1” corresponding to each pair of the fourth to fourth rows is acquired. Here, the test data generation apparatus 100 determines that the acquired execution result 2202 “0, 0, −1, −1” and the execution result 2201 “{0 of each pair of the first to fourth rows included in the test data are included. , 1}, 0, −1, 0 ”.

  Here, the test data generation device 100 determines that the acquired execution result 2202 for the pair in the first row is included in the execution result 2201 included in the test data. For this reason, the test data generation device 100 does not determine that the function of the method has been changed in the determination for the pair in the first row.

  Next, the test data generation device 100 determines that the acquired execution result 2202 matches the execution result 2201 included in the test data for the pair of the second row and the third row. For this reason, the test data generation device 100 does not determine that the function of the method has been changed in the determination for each pair of the first row and the third row.

  Next, the test data generation device 100 determines that the acquired execution result 2202 for the fourth row is not included in the execution result 2201 included in the test data. Then, the test data generation device 100 refers to the same determination group data 2100 and refers to the determination result for the second row pair included in the group because the fourth row pair is included in the grouped group. Here, the test data generation apparatus 100 does not determine that the function of the method is changed in the determination for the pair in the fourth row because the determination result for the pair in the second row matches.

  As described above, the test data generation device 100 cannot determine that the function of the method has been changed in the determination for each pair of the first to fourth lines, and thus determines that the function of the method has not been changed. In addition, when the test data generation device 100 determines that the function of the method has been changed in any of the determinations for each pair of the first to fourth lines, the test data generation device 100 determines that the function of the method has been changed. As a result, the test data generation device 100 can execute the regression test even in the changed program P2 in which the program variable or the parameter variable is changed.

(Regression test execution procedure)
Next, an example of a regression test execution process procedure of the test data generation apparatus 100 will be described with reference to FIG.

  FIG. 23 is a flowchart illustrating an example of a procedure for executing a regression test. In FIG. 23, the test data generating apparatus 100 acquires a pre-change program P1 and a post-change program P2 (step S2301). Next, the test data generation device 100 executes a specific process to be described later with reference to FIG. 24 (step S2302). Then, the test data generation device 100 executes processing described later with reference to FIG. 25 (step S2303).

  Next, the test data generation device 100 executes a test data generation process which will be described later with reference to FIG. 26 (step S2304). Then, the test data generation device 100 executes a refactoring process to be described later with reference to FIG. 27 (step S2305).

  Next, the test data generation device 100 executes a regression test (step S2306). The content of the regression test has been described above with reference to FIG. Then, the test data generation device 100 ends the execution process. Thereby, the test data generation device 100 can execute the regression test on the changed program P2 in which the variable is changed.

(Difference information specific processing procedure)
Next, an example of the difference information specifying process procedure of the test data generation apparatus 100 will be described with reference to FIG.

  FIG. 24 is a flowchart illustrating an example of the difference information identification processing procedure. In FIG. 24, the test data generation apparatus 100 extracts program variables and parameter variables of the test target method in the pre-change program P1 (step S2401). Next, the test data generation device 100 extracts program variables and parameter variables of the test target method in the changed program P2 (step S2402).

  Then, the test data generation device 100 extracts the program variables and parameter variables having the invariant names before and after the change from the pre-change program P1 to the post-change program P2 from the program variables and parameter variables extracted in steps S2401 to S2402. (Step S2403).

  Next, the test data generation apparatus 100 obtains additional candidate program variables and parameter variables before and after the change from the pre-change program P1 to the post-change program P2 from the program variables and parameter variables extracted in steps S2401 to S2402. Extract (step S2404).

  Then, the test data generation device 100 extracts deletion candidate program variables and parameter variables before and after the change from the pre-change program P1 to the post-change program P2 from the program variables and parameter variables extracted in steps S2401 to S2402. (Step S2405).

  Next, the test data generating apparatus 100 adds dummy variables to the set of additional candidate program variables and parameter variables and the set of deletion candidate program variables and parameter variables extracted in steps S2404 to S2405 ( Step S2406).

  Then, the test data generating apparatus 100 changes the data based on the set of addition candidate program variables and parameter variables and the set of deletion candidate program variables and parameter variables to which dummy variables have been added in step S2406. A pattern is specified (step S2407).

  Next, the test data generation device 100 changes the change pattern that is changed from the dummy variable to the dummy variable and the change pattern that is changed from the variable to another variable having a different type among the change patterns identified in step S2407. Are removed (step S2408). Then, the test data generation device 100 classifies the change patterns for each definition area (step S2409).

  Next, the test data generation device 100 identifies all variations by combining the change patterns of the program variables and the parameter variables of the pre-change program P1 among the change patterns classified for each domain in step S2409 ( Step S2410).

  Then, the test data generating apparatus 100 adds a change pattern that does not overlap the range to the change variation specified in step S2410 (step S2411), and ends the specifying process. Thereby, the test data generation device 100 can specify a program change variation.

(Program processing procedure)
Next, an example of the processing procedure of the program of the test data generation device 100 will be described with reference to FIG.

  FIG. 25 is a flowchart illustrating an example of a processing procedure of a program. In FIG. 25, the test data generating apparatus 100 selects a change variation (step S2501). Next, the test data generation device 100 creates a program variation from the pre-change program P1 based on the change variation selected in step S2501 (step S2502).

  Then, the test data generation device 100 determines whether there is an unselected change variation (step S2503). Here, when there is an unselected change variation (step S2503: Yes), the test data generation device 100 returns to the process of step S2501.

  On the other hand, when there is no unselected change variation (step S2503: No), the test data generation device 100 ends the processing. Thereby, the test data generation device 100 can create a program variation.

(Test data generation processing procedure)
Next, an example of a test data generation process procedure of the test data generation apparatus 100 will be described with reference to FIG.

  FIG. 26 is a flowchart illustrating an example of a test data generation processing procedure. In FIG. 26, the test data generating apparatus 100 selects a program variation (step S2601). Next, the test data generation device 100 creates test data from the program variation selected in step S2601 (step S2602).

  Then, the test data generation device 100 determines whether there is an unselected program variation (step S2603). If there is an unselected program variation (step S2603: YES), the test data generation device 100 returns to the process of step S2601.

  On the other hand, when there is no unselected program variation (step S2603: No), the test data generation device 100 ends the test data generation process. Thereby, the test data generation device 100 can create test data.

(Refactoring procedure)
Next, an example of the refactoring processing procedure of the test data generation device 100 will be described with reference to FIG.

  FIG. 27 is a flowchart illustrating an example of a refactoring processing procedure. In FIG. 27, the test data generating apparatus 100 deletes the item of the deletion candidate variable included in the test data (step S2701). Next, the test data generation device 100 merges the test case and execution result pair of the test data (step S2702).

  Then, the test data generation apparatus 100 integrates pairs in which test cases overlap among the merged test data (step S2703). Next, the test data generation apparatus 100 groups pairs in which the program variable and parameter variable of the invariant name in the test case have the same value and the execution result has the same value (step S2704). ). Then, the test data generation device 100 ends the refactoring process.

  As described above, according to the test data generation apparatus 100, the variable change pattern is specified from the pre-change program P1 and the post-change program P2, and a program variation for generating test data of the post-change program P2 is created. Can do. Then, according to the test data generating apparatus 100, it is possible to generate the test data of the changed program P2 based on the program variation.

  Thereby, the test data generation device 100 can generate test data used for the regression test even for the changed program P2 including the change of the program variable or the parameter variable. For this reason, for example, compared with a case where the execution person of the regression test refers to the program P1 before change and the program P2 after change and creates test data for the program P2 after change, the work of the person who executes the regression test The amount can be reduced. Further, it is possible to prevent erroneous test data from being created by a regression tester.

  Moreover, according to the test data generation device 100, at least one of a change pattern that is changed from a variable to another variable of the same format, a change pattern that deletes a variable, and a change pattern that adds a variable is selected. Can be identified. As a result, the test data generation apparatus 100 can minimize the number of variable patterns by preventing the change patterns used for creating the program variations from overlapping. Therefore, the test data generation device 100 can suppress an increase in the processing amount of the program variation creation process.

  Moreover, according to the test data generation device 100, a plurality of program variations can be created based on a combination of a plurality of change patterns. As a result, the test data generating apparatus 100 can create a program variation for generating test data of the changed program P2. Further, according to the test data generating apparatus 100, a plurality of program variations can be created based on combinations of different variable change patterns. Thereby, the test data generation device 100 can prevent duplication of combinations of change patterns, and can suppress an increase in the processing amount of the program variation creation process.

  In addition, according to the test data generation apparatus 100, test data can be automatically generated by symbolically executing a program variation. Further, the test data generation apparatus 100 can execute a regression test using the test data. As a result, the test data generation apparatus 100 can execute the regression test on the changed program P2 after changing the program variable or the parameter variable, and can output the test result of the regression test. it can. Then, the user of the test data generation device 100 can grasp whether or not there is a problem in the changed program P2 from the test result of the regression test.

  Note that the test data generation method described in the present embodiment can be realized by executing a program prepared in advance on a computer such as a personal computer or a workstation. The test data generation program is recorded on a computer-readable recording medium such as a hard disk, a flexible disk, a CD-ROM, an MO, and a DVD, and is executed by being read from the recording medium by the computer. The test data generation program may be distributed through a network such as the Internet.

  The following additional notes are disclosed with respect to the embodiment described above.

(Supplementary note 1)
A comparison result obtained by comparing a variable used for the method in the first program in which the method to be tested is defined with a variable used in the method in the second program in which the content of the method is changed from the first program. Based on the variable change pattern used in the method,
Based on the identified change pattern, create a third program in which variables used for the method in the first program are changed,
Generate test data by executing symbolically the created third program,
A test data generation program characterized by causing a process to be executed.

(Supplementary note 2)
Regarding variables used in the method, a change pattern that is changed to another variable of the same format after the change of the content of the method, a change pattern that is deleted after the change of the content of the method, and a change of the content of the method The test data generation program according to appendix 1, wherein at least one of the change patterns to be added is specified.

(Supplementary Note 3) The test data generation program according to Supplementary Note 1 or 2, wherein the variable used in the method is a method parameter that is an argument of the method or a variable other than the argument of the method.

(Appendix 4) The process to create is
4. The test data generation program according to any one of appendices 1 to 3, wherein the third program is created based on a combination of change patterns specified for each variable used in the method.

(Appendix 5) The process to create is
The test data generation program according to appendix 4, wherein the third program is created based on a combination of change patterns for different variables used in the method.

(Appendix 6) The process to generate is
Additional test 4 or 5 is characterized in that the test data having the value of the input variable that becomes the execution condition of the series of statements in the generated third program and the value of the output variable that becomes the execution result are generated. The test data generation program described.

(Appendix 7)
Determining whether the value of the output variable when the second program is executed using the value of the input variable of the generated test data as an execution condition matches the value of the output variable of the test data;
The test data generation program according to appendix 6, wherein the process is executed.

(Supplementary note 8)
Determining whether the value of the output variable when the second program is executed using the value of the input variable of the generated test data as an execution condition is included in the value of the output variable of the test data The test data generation program according to appendix 7, characterized by:

(Supplementary note 9)
Of the pairs that associate the execution conditions and the execution results of the generated test data, the input variables that are not renamed before and after the method change among the input variables that are the execution conditions are the same. And execute the process of grouping pairs with the same execution result,
The determination process is as follows:
Among the grouped pairs, the value of the output variable when the second program is executed using the value of the input variable included in any pair as the execution condition is the value of the output variable included in any of the pairs 9. The test data generation program according to appendix 7 or 8, wherein it is determined whether or not it is included in the test data.

(Supplementary note 10)
A comparison result obtained by comparing a variable used for the method in the first program in which the method to be tested is defined with a variable used in the method in the second program in which the content of the method is changed from the first program. Based on the variable change pattern used in the method,
Based on the identified change pattern, create a third program in which variables used for the method in the first program are changed,
Generate test data by executing symbolically the created third program,
A test data generation method characterized by executing processing.

(Additional remark 11) The variable used for the said method in the 1st program in which the test object method was prescribed | regulated, and the variable used for the said method in the 2nd program from which the content of the said method was changed from the said 1st program Creating a third program in which a variable change pattern used in the method is specified based on the comparison result, and a variable used in the method in the first program is changed based on the specified change pattern And
A generating unit that symbolically executes the third program created by the creating unit to generate test data;
A test data generation apparatus comprising:

DESCRIPTION OF SYMBOLS 100 Test data production | generation apparatus 301 Specification part 302 Creation part 303 Generation part 304 Determination part

Claims (10)

On the computer,
A comparison result obtained by comparing a variable used for the method in the first program in which the method to be tested is defined with a variable used in the method in the second program in which the content of the method is changed from the first program. Based on the variable change pattern used in the method,
Based on the identified change pattern, create a third program in which variables used for the method in the first program are changed,
Generate test data by executing symbolically the created third program,
A test data generation program characterized by causing a process to be executed. The process to specify is
Regarding variables used in the method, a change pattern that is changed to another variable of the same format after the change of the content of the method, a change pattern that is deleted after the change of the content of the method, and a change of the content of the method 2. The test data generation program according to claim 1, wherein at least one of the change patterns to be added is specified.   The test data generation program according to claim 1, wherein the variable used in the method is a method parameter that is an argument of the method or a variable other than the argument of the method. The process to create is
The test data generation program according to any one of claims 1 to 3, wherein the third program is created based on a combination of change patterns specified for each variable used in the method. The process to generate is
5. The test data having an input variable value that becomes an execution condition of a sequence of statements in the created third program and an output variable value that becomes an execution result in association with each other is generated. Test data generation program. In the computer,
Determining whether the value of the output variable when the second program is executed using the value of the input variable of the generated test data as an execution condition matches the value of the output variable of the test data;
The test data generation program according to claim 5, wherein the process is executed. The determination process is as follows:
Determining whether the value of the output variable when the second program is executed using the value of the input variable of the generated test data as an execution condition is included in the value of the output variable of the test data The test data generation program according to claim 6. In the computer,
Of the pairs that associate the execution conditions and the execution results of the generated test data, the input variables that are not renamed before and after the method change among the input variables that are the execution conditions are the same. And execute the process of grouping pairs with the same execution result,
The determination process is as follows:
Among the grouped pairs, the value of the output variable when the second program is executed using the value of the input variable included in any pair as the execution condition is the value of the output variable included in any of the pairs The test data generation program according to claim 6, wherein it is determined whether or not it is included in the test data. Computer
A comparison result obtained by comparing a variable used for the method in the first program in which the method to be tested is defined with a variable used in the method in the second program in which the content of the method is changed from the first program. Based on the variable change pattern used in the method,
Based on the identified change pattern, create a third program in which variables used for the method in the first program are changed,
Generate test data by executing symbolically the created third program,
A test data generation method characterized by executing processing. A comparison result obtained by comparing a variable used for the method in the first program in which the method to be tested is defined with a variable used in the method in the second program in which the content of the method is changed from the first program. Based on the specified change pattern of the variable used in the method, based on the specified change pattern, a creation unit that creates a third program that changes the variable used in the method in the first program;
A generating unit that symbolically executes the third program created by the creating unit to generate test data;
A test data generation apparatus comprising:

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