CN109344057B - Combined acceleration test case generation method based on genetic method and symbolic execution - Google Patents
Combined acceleration test case generation method based on genetic method and symbolic execution Download PDFInfo
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Abstract
A combined acceleration test case generation method based on genetic method and symbolic execution comprises the following steps: 1: acquiring a program to be tested of a software test; 2: carrying out selection, mutation and cross operation; acquiring a termination coverage rate, and recording as a first coverage rate; 3: acquiring a path which can be reached by a first test case, and recording the path as a first path set; acquiring all executable paths of a program to be tested, and recording as a second path set; 4: comparing the first path set with the second path set, and determining the uncovered path of the first test case as a third path set; 5: solving the value combination corresponding to the target path; 6: testing the termination coverage rate of the second test case, and recording as a second coverage rate; 7: evaluating by combining the first coverage rate and the second coverage rate, and repeating the steps 2-6 under the condition of not reaching the standard; the invention realizes the symbol extraction of the current path and the value combination of all the conditions, thereby realizing the combination and the iteration of the genetic method and the symbol execution, generating the use case with high quality and having high coverage rate.
Description
Technical Field
The invention provides a combined acceleration test case generation method based on a genetic method and symbolic execution, and belongs to the technical field of software testing.
Background
With the continuous development of software technology, in the face of errors possibly existing in a software system and the problems of low quality and low reliability of the software system, the testing technology comes along, the optimization of software testing is taken as an important direction of the research of the testing technology, and the selection of a good test case set can not only reduce the workload of software testing, but also improve the speed of software development. Therefore, how to generate as few test cases as possible and improve the detection capability of defects in software testing, and reducing the testing cost is the core of problems in the field of software testing optimization research.
Currently, genetic methods can be used to generate test cases. The method adopts the space search solving capability of a genetic method to automatically generate a group of test cases meeting the conditions. In the operation process, the test program is based on the operation of the current population individuals as an input drive program, the condition of meeting the test target path is taken as the target function of the method, the path executed by the test case generated by each generation of the method is continuously recorded and tracked, the iterative method is continuously evolved, and one generation of individuals and another generation of individuals are generated until a correct solution is found. However, the method has the defects that the use case generation process is easy to fall into local convergence, so that the use case generation can be stopped in a few genetic generations, the operation of crossing and selecting in the genetic method is disabled, and the coverage rate often cannot meet the requirement. Thus, there is a need for a method that addresses the problem of this type of population aging in genetic methods that does not achieve high coverage.
Disclosure of Invention
Object (a)
The invention discloses a combined acceleration test case generation method based on a genetic method and symbolic execution, which can solve the problem that in the automatic generation process of cases in the prior art, population aging is easy to fall into precocity, so that the operation of crossing and selecting in the genetic method is disabled, thereby realizing the generation of as few test cases as possible in a short time, achieving higher coverage rate in software test and reducing the test cost.
(II) technical scheme
The invention provides a combined accelerated test case generation method based on a genetic method and symbolic execution, which comprises the following implementation steps of:
step 1: acquiring a program to be tested of a software test;
step 2: generating a first test case based on a genetic method, and carrying out selection, variation and cross operation according to a fitness function until a termination condition of population evolution is reached; acquiring the termination coverage rate of the first test case, and recording as a first coverage rate;
and step 3: obtaining a path which can be reached by the first test case and recording as a first path set; acquiring all executable paths of the program to be tested, and recording as a second path set;
and 4, step 4: comparing the first path set with the second path set, and determining that the uncovered path of the first test case is marked as a third path set;
and 5: determining a target path based on the third path set, and solving a value combination corresponding to the target path by using a symbolic execution method;
step 6: generating a second test case by using a genetic method based on the value combination, testing the termination coverage rate of the second test case, and recording as a second coverage rate;
and 7: evaluating the test case by combining the first coverage rate and the second coverage rate, and repeating the steps 2-6 under the condition that the coverage rate does not reach the standard;
through the steps, the symbol extraction of the current path is carried out on the uncovered path in the use case generated by the genetic method through the symbol execution method, the value combination of all the conditions meeting the constraint set in the symbol execution is solved, the genetic method can continue to execute the generated use case based on the value combination, and therefore the combination and the iteration of the genetic method and the symbol execution are realized, the use case with high quality can be generated in a short time, and the coverage rate is high.
The step 1 of acquiring the program to be tested of the software test comprises the following specific implementation steps: a Siemens test program set is adopted as a program to be tested; the program to be tested has the characteristics of multiple required branches and multiple judgments, and in order to prevent accidental occurrence, the program in the program set to be tested can be operated for multiple times to be averaged to obtain the basic attributes of the program, wherein the basic attributes at least comprise: process number, total number of rows, loop complexity, and termination coverage.
Generating a first test case based on a genetic method in the step 2, and performing selection, variation and cross operation according to a fitness function until a termination condition of population evolution is reached; acquiring the termination coverage rate of the first test case, and recording the termination coverage rate as a first coverage rate, wherein the specific steps are as follows: adding information of a program to be tested and a method into an application program to generate a test case, wherein the application program can return a coverage value of the test case to a fitness function, the genetic method performs selection, variation and cross operation based on the fitness function to generate a next generation chromosome group, the operation is circulated until a termination condition of population evolution is reached, and the coverage value of the test case is recorded as a first coverage rate; recording an evolutionary population algebra and a corresponding population coverage rate in the evolution process, wherein when the evolutionary algebra is increased by n generations from a certain algebra (for example, m), the coverage rate is not improved any more, namely the coverage rate of a test case after evolving m + n generations is the same as that of the test case after evolving m generations, the evolution is set to be terminated when evolving m + n generations, and executing a symbol execution solution and subsequent steps; in another simple embodiment, the setting process of the termination condition may be that based on a large amount of sample data of test case examples generated based on the genetic method, an evolution algebra is estimated, in which the coverage rate is not increased any more when the genetic method reaches a certain algebra, the algebra is set as the termination condition, that is, when the algebra is evolved, the genetic method is stopped to generate the test cases, and the execution of symbolic execution solving and subsequent steps are started; before the test case is generated, logic flow analysis can be performed on the program to be tested, the value range of an input variable in the program, namely an input space, can be deduced based on a certain rule, correspondingly, the initial population of the genetic method can be generated randomly in the input space, and the coverage rate type can be selected from the most common branch coverage.
Wherein, in step 3, the "path that can be achieved by obtaining the first test case" is recorded as a first path set; acquiring all executable paths of the program to be tested, and recording as a second path set, and specifically comprising the following steps: and selecting any programming language and static analysis tool, and executing a function call statement to obtain a path which can be reached by the first test and all executable paths of the program to be tested, and respectively marking as a first path set and a second path set.
Wherein, in the step 4, the step of comparing the first path set with the second path set to determine that the uncovered path of the first test case is marked as the third path set comprises the following specific steps: and leading the first path set and the second path set into an analysis tool, and comparing, screening and processing the two sets to obtain paths in the second path set except for the first path set, namely paths uncovered by the first test case.
Wherein, in step 5, "determining a target path based on the third path set, and solving a value combination corresponding to the target path by using a symbolic execution method" specifically includes the following steps: obtaining a shorter path from the third path set as a target path, in a preferred embodiment, obtaining a shortest path by using a directed graph and outputting the shortest path as the target path, representing variables, input data and constraint conditions corresponding to the target path by using symbols, establishing a one-to-one correspondence relationship with abstract symbol table entries, forming the symbols into a constraint set, and obtaining a value combination meeting all conditions in the constraint set, wherein the value combination can be one or more groups; solving the set of constraints includes: pre-judging a path, simplifying a constraint expression, simplifying a constraint set and storing an unexecuted path; the path is prejudged to prevent path explosion; in a solving mode, an optimization method is adopted to convert constraint conditions into propositional logic form, and then a solver is called to solve to obtain value combinations meeting the constraint conditions.
Wherein, the step 6 of generating a second test case by using a genetic calculation method based on the value combination, testing the termination coverage rate of the second test case, and recording as a second coverage rate comprises the following specific steps: a group of value combinations can be selected as an initial population in the genetic method to further generate the test case, and in the generation process, the termination coverage rate of a second test case is obtained and recorded as a second coverage rate; the condition for terminating the test case generation step can be that the generated test case meets a certain code coverage criterion or the iteration number reaches a preset value.
Wherein, in step 7, the test case is evaluated in combination with the first coverage and the second coverage, and the steps 2 to 6 are repeated when the coverage does not meet the standard, and the specific steps are as follows: recording the first coverage rate as p1, recording the second coverage rate as p2, recording the final coverage rate of the test case passing the steps 2-6 as p, comparing the p1 and the p2 with the p, obtaining the increase condition of the coverage rate of the test case generated based on symbol execution, comparing the final coverage rate p with the expected coverage rate, and repeating the steps 2-6 under the condition that the coverage rate p does not reach the standard until the generated test case reaches the expected coverage rate.
(III) advantages and effects
The technical scheme provided by the embodiment of the invention has the beneficial effects that at least:
the method comprises the steps of extracting symbols of a current path from uncovered paths in use cases generated by a genetic method through symbolic execution, solving all value combinations meeting all conditions of a constraint set in symbolic execution, enabling the genetic method to continue to execute the generated use cases based on the value combinations, and accordingly realizing combination and iteration of the genetic method and symbolic execution, generating use cases with high quality in a short time and having high coverage rate. The generation method is simple and practical, is easy to implement and has popularization and application values.
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In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a combined accelerated test case generation method based on a genetic method and symbolic execution according to an embodiment of the present invention.
Fig. 2 is a schematic diagram of a combined accelerated test case generation method based on a genetic method and symbolic execution according to an embodiment of the present invention.
Fig. 3 is a schematic diagram of constraint solving in a combined accelerated test case generation method based on a genetic method and symbolic execution according to an embodiment of the present invention.
Detailed Description
The invention provides a combined acceleration test case generation method based on a genetic method and symbolic execution, and in order to make the purposes, technical schemes and advantages of the invention clearer, the following describes the embodiment of the invention in detail with reference to the attached figures 1-3.
The invention relates to a combined acceleration test case generation method based on a genetic method and symbolic execution, which comprises the following implementation steps as shown in figure 1:
101. and acquiring a program to be tested of the software test.
The program under test may refer to a set of programs for performing software testing, and generation and evaluation of test cases may be performed based on the program. According to the technical scheme, a Siemens test program set can be used as a test program. Of course, the program with more branches and more decision nodes can be used as the program to be tested. In one embodiment, to avoid the occurrence of contingency, the basic attributes of a program in a program set to be tested may be obtained by averaging multiple runs of the program, where the basic attributes at least include: process number, total number of rows, loop complexity, and termination coverage. In one embodiment, a suitable set of variable values may be selected from the definition domain of the program variable as the program input, and the values of the input variables are adjusted based on the difference between the operation result and the expected result, so that the execution result gradually meets the corresponding requirements of the software test.
102. Generating a first test case based on a genetic method, and carrying out selection, variation and cross operation according to a fitness function until a termination condition of population evolution is reached; and acquiring the termination coverage rate of the first test case, and recording as a first coverage rate.
The genetic method refers to a process of carrying out selection, mutation and cross operation based on a fitness function to generate a next generation chromosome population. The process of specifically generating the test case may include: and adding the information of the program to be tested and the method into an application program to generate a test case, wherein the application program can return the coverage value of the test case to a fitness function, and after the test case is generated, the coverage value of the test case is recorded as a first coverage. In one embodiment, before generating the test case, the logic flow analysis may be performed on the program to be tested, and the data value that must be input is derived based on a certain rule, wherein the initial population of the genetic method may be generated randomly, and the most common branch coverage is selected for each coverage type. Certainly, the initial population can also automatically generate a group of test cases meeting the conditions by adopting the space search solving capability of the genetic method, and in the method operation process, the current population individuals are used as input, a program is driven to operate, and the path executed by the test case generated by each generation of the method is recorded and tracked, so that the iterative method is continuously evolved.
103. Obtaining a path which can be reached by the first test case and recording as a first path set; and acquiring all executable paths of the program to be tested and recording as a second path set.
Any programming language and static analysis tool can be selected, and a function call statement is executed to obtain a path reachable by the first test case and all executable paths of the program to be tested, and the paths are marked as a first path set and a second path set respectively.
In an embodiment, the feature set of the program path and the related inference rule may also be defined by statically analyzing the data flow and control flow structure of the program, and the state of each program node in the execution is automatically inferred by using the path features of the program execution, so as to provide a basis for extracting the uncovered path in step 4.
104. And comparing the first path set with the second path set, and determining that the uncovered paths of the first test case are marked as a third path set.
In the step, the first path set and the second path set are introduced into an analysis tool, comparison, screening and processing between the two sets are performed, and paths in the second path set except for the first path set, namely paths uncovered by the first test case, are obtained.
105. And determining a target path based on the third path set, and solving a value combination corresponding to the target path by using a symbolic execution method.
Representing variables, input data and constraint conditions corresponding to the target path by using symbols, establishing a one-to-one correspondence relationship with abstract symbol table items, forming the symbols into a constraint set, and solving value combinations meeting all conditions in the constraint set, wherein the value combinations can be one group or multiple groups. In one embodiment, the variables and input data used in the third path set are in one-to-one correspondence with a symbol table managed by a symbol execution tool, wherein the symbol execution tool is used for tracking the operation of the variables and data by a program and establishing path constraint conditions by using expressions in the table at a program branch. In one possible design, the symbol execution tool KLEE may be used to represent the symbol table using objects of mcmorryobject, mapping all the memory space allocated in the program.
In one embodiment, solving the set of constraints comprises: the method comprises the steps of path prejudging, constraint expression simplification, constraint set simplification, unexecuted path saving and the like. The path anticipation is used to prevent path explosion. In a solving mode, an optimization method is adopted to convert constraint conditions into propositional logic form, and then a solver is called to solve to obtain value combinations meeting the constraint conditions.
In another embodiment, steps 3-5 may be replaced by the following steps: the method may further include generating a constraint set of the current path by using a symbolic execution method based on the first path set, and further processing the corresponding constraint conditions, where the processing may include negating one or more constraint conditions in the current path, that is, negating the constraint condition expression by selecting different strategies, performing further generation of the test case based on the current path after processing the constraint conditions by using a genetic method.
106. And generating a second test case by using a genetic method based on the value combination, and testing the termination coverage rate of the second test case and recording as a second coverage rate.
And selecting a group of value combinations as an initial population in the genetic method to further generate the test case, and obtaining the termination coverage rate of a second test case in the generation process and marking as a second coverage rate. The condition for terminating the test case generation step can be that the generated test case meets a certain code coverage criterion or the iteration number reaches a preset value.
107. And evaluating the test case by combining the first coverage rate and the second coverage rate, and repeating the steps 2-6 under the condition that the coverage rate does not reach the standard.
The evaluation method comprises the following steps: and marking the first coverage rate as p1 and the second coverage rate as p2, comparing p1, p2 and p to obtain the increase condition of the coverage rate of the test case generated based on symbolic execution, comparing the final coverage rate p with the expected coverage rate, and repeating the steps 2-6 under the condition that the coverage rate p does not reach the expected coverage rate until the generated test case reaches the expected coverage rate.
In another possible design, when the case generation method based on the combination of symbolic execution and genetic method cannot achieve a more significant improvement in coverage after two iterations, another combination mode may be tried to be performed, for example, a case generation method based on a regeneration genetic method may be selected, that is, a method for generating a test case by using a regeneration differential evolution method is used when the termination coverage of a second test case is still not high.
Claims (1)
1. A combined acceleration test case generation method based on genetic method and symbolic execution is characterized in that: the implementation steps are as follows:
step 1: acquiring a program to be tested of a software test;
step 2: generating a first test case based on a genetic method, and carrying out selection, variation and cross operation according to a fitness function until a termination condition of population evolution is reached; acquiring the termination coverage rate of the first test case, and recording as a first coverage rate;
and step 3: obtaining a path which can be reached by the first test case, and recording the path as a first path set; acquiring all executable paths of the program to be tested, and recording as a second path set;
and 4, step 4: comparing the first path set with the second path set, and determining that the uncovered path of the first test case is marked as a third path set;
and 5: determining a target path based on the third path set, and solving a value combination corresponding to the target path by using a symbolic execution method;
step 6: generating a second test case by using a genetic method based on the value combination, testing the termination coverage rate of the second test case, and recording as a second coverage rate;
and 7: evaluating the test case by combining the first coverage rate and the second coverage rate, and repeating the steps 2-6 under the condition that the coverage rate does not reach the standard;
the step 1 of obtaining the program to be tested for the software test comprises the following specific implementation steps: a Siemens test program set is adopted as a program to be tested; the method has the characteristics of multiple required branches and multiple judgments of the program to be tested, and can average the program operation in the program set to be tested for multiple times to obtain the basic attribute of the program, wherein the basic attribute comprises the following steps: process number, total number of rows, number of turns complexity and termination coverage;
generating a first test case based on the genetic method in the step 2, and performing selection, variation and cross operation according to the fitness function until a termination condition of population evolution is reached; acquiring the termination coverage rate of the first test case, and recording the termination coverage rate as a first coverage rate, wherein the specific steps are as follows: adding information of a program to be tested and a method into an application program, generating a test case, returning a coverage value of the test case to a fitness function by the application program, performing selection, variation and cross operation on the genetic method based on the fitness function, generating a next generation chromosome group, circulating the operation until a termination condition of population evolution is reached, and recording the coverage value of the test case as a first coverage rate; recording an evolutionary population algebra and a corresponding population coverage rate in the evolution process, wherein the evolutionary algebra starts from an algebra; after m is increased by n generations, the coverage rate is not improved any more, namely the coverage rate of the test case after evolving m + n generations is the same as that of the test case after evolving m generations, the evolution is set to be terminated when evolving m + n generations, and the symbolic execution solution and the subsequent steps are executed; based on a large amount of sample data of test case examples generated based on the genetic method, estimating an evolution algebra with coverage rate not being improved when the genetic method reaches an algebra, and setting the algebra as a termination condition, namely, when the algebra is evolved, stopping generating the test case by the genetic method, and starting executing symbolic execution solving and subsequent steps; before generating the test case, firstly, carrying out logic flow analysis on the program to be tested, deducing a value range of an input variable in the program based on a preset rule, namely an input space, correspondingly, adopting a random generation mode in the input space for an initial population of the genetic method, and selecting branch coverage by a coverage rate type;
wherein, the path that can be achieved by the first test case is obtained in step 3 and is marked as a first path set; acquiring all executable paths of the program to be tested, and recording as a second path set, wherein the specific steps are as follows: selecting any programming language and static analysis tool, and executing a function call statement to obtain a path which can be reached by the first test and all executable paths of the program to be tested, and respectively recording the paths as a first path set and a second path set;
comparing the first path set with the second path set in step 4, and determining that the uncovered path of the first test case is marked as a third path set, which specifically comprises the following steps: the method comprises the steps that a first path set and a second path set are led into an analysis tool, comparison, screening and processing between the two sets are carried out, and paths in the second path set except for the first path set, namely paths uncovered by a first test case, are obtained;
in step 5, a target path is determined based on the third path set, and a value combination corresponding to the target path is solved by using a symbolic execution method, specifically including the following steps: acquiring a short path from the third path set as a target path, obtaining the shortest path by adopting a directed graph, outputting the path, marking the path as the target path, representing variables, input data and constraint conditions corresponding to the target path by using symbols, establishing a one-to-one correspondence relationship with abstract symbol table items, forming the symbols into a constraint set, and solving a value combination meeting all conditions in the constraint set, wherein the value combination is a group and a complex group; solving the set of constraints includes: pre-judging a path, simplifying a constraint expression, simplifying a constraint set and storing an unexecuted path; the path is prejudged to prevent path explosion; converting the constraint conditions into propositional logic form by adopting an optimization method, and then calling a solver to solve to obtain a value combination meeting the constraint conditions;
generating a second test case by using a genetic calculation method based on the value combination in the step 6, testing the termination coverage rate of the second test case, and recording the termination coverage rate as a second coverage rate, wherein the specific steps are as follows: selecting a group of value combinations as an initial population in a genetic method, generating a test case, and obtaining termination coverage rate of a second test case in the generation process and marking as a second coverage rate; the test case generation step is terminated under the conditions that the generated test case meets the code coverage criterion and the iteration times reach a preset value;
wherein, in step 7, the test case is evaluated by combining the first coverage rate and the second coverage rate, and the steps 2 to 6 are repeated under the condition that the coverage rates do not reach the standard, and the specific steps are as follows: recording the first coverage rate as p1, recording the second coverage rate as p2, recording the final coverage rate of the test case passing the steps 2-6 as p, comparing the p1 and the p2 with the p, obtaining the increase condition of the coverage rate of the test case generated based on symbol execution, comparing the final coverage rate p with the expected coverage rate, and repeating the steps 2-6 under the condition that the coverage rate p does not reach the standard until the generated test case reaches the expected coverage rate.
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