CN117951001A - Mutation test generation method, system and computer storage medium - Google Patents
Mutation test generation method, system and computer storage medium Download PDFInfo
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Abstract
The invention provides a variation test generation method, a variation test generation system and a computer storage medium, wherein an adaptability model is built according to a constructed control flow, a data flow and a signal flow constraint function; and iterating the fitness model, if the iteration result meets the termination condition, outputting a variation result, otherwise, continuing iterating. The advantages are that: based on control flow constraint, combining data flow and signal flow constraint, constructing a new fitness model, and improving variation scores in an evaluation system of test case quality measurement. When the signal flow constraint function is constructed, a signal threshold envelope constraint boundary table is added, so that the accuracy of the interactive signal boundary designed by the variation test case is improved, and the misjudgment rate of the variation test on the functional test case of the automobile safety airbag module is reduced. And carrying out genetic operation on the fitness model, and using the change amount of the fitness of two adjacent generations as a threshold value, so as to intervene in suppressing population iteration, effectively avoiding premature genetic algorithm and reducing iteration times.
Description
Technical Field
The present invention relates to the field of electronic control systems, and in particular, to a mutation test generation method, a mutation test generation system, and a computer storage medium.
Background
Software testing has a significant impact on the quality and productivity of automotive software products. The mutation test is an effective software test method and can be used for evaluating the sufficiency of test cases and assisting in enhancing the test cases.
Because of the requirement of functional safety, the vehicle electronic control module has redundant design of multiple software and hardware like an airbag controller, and has higher requirements on performance indexes such as control logic adaptability, data transmission stability, signal transmission reliability and the like of software in various complex scenes besides functional coverage such as software ignition, the vehicle functional safety software has huge code quantity and complex logic, development of software integration test is not easy, the generation quality of test cases is poor, the accuracy of the software integration test cannot be ensured, and test results are not available after manpower and material resources are input.
The mutation test is a quick and effective means for evaluating the test sufficiency and enhancing the quality of the test case in the test design process. The traditional automatic variation test generation method such as CDG, CFG and the like is mainly used for generating variation test cases aiming at software unit tests, and in the test practice of an airbag controller and the like, the problems that judgment conditions and expressions which depend on input variables are difficult to process, the correctness of the airbag error action test cases cannot be checked due to lack of data flow constraint, population genetic stagnation in the variation case process and the like are caused, the scoring of the test cases is low, the algorithm iteration times are excessive and the like exist.
Specifically, the existing method has the following problems after analysis by using actual test data:
1) The intra-industry mutation test is mostly used for detecting the integrity and coverage rate of test cases in unit test, and a mutation test model generated by traditional code structural mutation is not applicable in the integrated test because of complex logic relation of functional modules.
2) The generation method based on single control constraint adopts the feasibility condition of control logic variant to establish constraint relation, is an efficient variant test case generation method, but is difficult to process judgment conditions and expressions which are dependent on input variables.
3) The path constraint is dynamically solved according to the control flow, so that the processing of judging conditions and expressions which are dependent on input variables is improved in the traditional method, but the constraint of data flow correctness and signal flow correctness existing in real scenes such as collision sensors in an air bag control module cannot be considered.
4) The traditional GA genetic algorithm builds an adaptability function model based on a control flow test sufficiency criterion, and tests various program structures. But in use it was found that such tests did not take into account data flow constraints, the test sufficiency was low.
5) When multiple targets such as data and signals are introduced, the first generation MOGA and NPGA multi-target optimization algorithm is easy to generate incomplete and too one-sided variant test cases, or the second generation NPGA-II and SPEA multi-target optimization sporadic solution sets with large code complexity are sparse or degenerate into random search cases and are difficult to converge, and the two are low in efficiency in the generation.
Therefore, how to provide a mutation test generation method capable of generating an effective test case, which has high test coverage, accuracy and reliability and a small number of iterations, is a problem to be solved.
Disclosure of Invention
The invention provides a variation test generation method, a variation test generation system and a computer storage medium, which are used for solving the problems that in the prior art, a variation test object is single, the test case is low in effectiveness, data streams existing in a real scene cannot be restrained, and a constraint relation cannot be established between judgment conditions and expressions which are dependent on input variables so that corresponding expressions cannot be generated for testing. The problem of multiple algorithm iteration times in the existing mutation test is further solved.
In order to achieve the above object, the present invention provides a mutation test generation method, including: constructing an adaptability model after constructing a control flow constraint function, a data flow constraint function and a signal flow constraint according to the acquired module architecture analysis result; calculating the fitness of the fitness model, and judging whether the test termination condition is met according to the fitness; if not, iterating the fitness model, judging whether the iteration termination condition is met according to the change of the fitness, if so, outputting a variation result, and if not, continuing iterating.
As the optimization of the technical scheme, the module architecture analysis result is preferably encoded to obtain the primary variation test case population.
As a preferred aspect of the above technical solution, preferably, constructing a control flow constraint function includes: and performing control flow statement approximate analysis on the module architecture analysis result, and constructing a control flow constraint function according to the analysis result.
As a preferred aspect of the above technical solution, preferably, performing control flow statement approximation analysis on a module architecture analysis result includes: and deleting the dead basic blocks in the block architecture analysis result, and scanning the triple address codes and the basic blocks in the block architecture analysis result to obtain a program control flow diagram. Constructing a control flow constraint function according to the program control flow graph; wherein the control flow restriction function is within the scope of the program control flow graph.
Preferably, as a preference of the above technical solution, constructing a control flow constraint function according to the program control flow graph includes: and taking the farthest path which accords with expected execution as control flow fitness, and obtaining control flow constraint in a control flow constraint function according to the control flow fitness.
Preferably, as a preference of the above technical solution, constructing a data flow constraint function includes: and carrying out data flow similarity analysis on the analysis result of the module architecture, determining data flow fitness according to the analysis result, and constructing a data flow constraint function, wherein the data flow constraint function is in the range of the obtained program data flow graph.
As a preferred aspect of the above technical solution, preferably, performing data flow similarity analysis on a module architecture analysis result includes: traversing after optimizing the analysis result of the module architecture to obtain a program data flow graph; determining the data flow fitness according to a program data flow graph; and obtaining the data flow constraint in the data flow constraint function according to the data flow adaptability. The data stream fitness is the variable path and the data boundary coverage, and the data refers to the variable value change coverage dimension.
Preferably, the sum of the data stream constraint function and the data stream constraint is 1.
As a preferred aspect of the foregoing solution, it is preferable to construct a signal flow constraint function, including: scanning input and output parameters in a module architecture analysis result to obtain a program signal flow diagram; constructing a signal flow constraint function according to the signal flow adaptability; wherein the signal flow restriction function is within the scope of the program signal flow graph.
Preferably, the signal threshold envelope constraint boundary table is constructed according to the signal stream adaptability, and the signal threshold envelope constraint boundary table is used as the constraint of the signal stream constraint function.
Preferably, the signal stream fitness is the maximum boundary that is suitable for the expected implementation.
Preferably, the constructing the fitness model includes: and performing weighted coupling on the control flow constraint function, the data flow constraint function and the signal flow constraint function to obtain the fitness model.
As a preferred aspect of the above technical solution, preferably, calculating the fitness of the fitness model, and determining whether the test termination condition is satisfied according to the fitness includes: calculating the fitness of the fitness function by adopting a primary variation test case population and adopting a non-dominant sorting algorithm, substituting 1, judging whether the current fitness model meets the test termination condition, scoring the current variation test if yes, and putting the result into a variation test case library; otherwise, enter the iteration.
As a preferred aspect of the above technical solution, preferably, determining whether the iteration termination condition is satisfied according to the change of the fitness includes: and iterating the first-generation variation test case population, and performing genetic operation according to the iterated variation test case population by the fitness model to obtain fitness. And comparing the fitness with the previous fitness, if the comparison result is smaller than the threshold value, continuing iteration without meeting the iteration termination condition, otherwise, judging whether the test termination condition is met.
Preferably, if the comparison result is smaller than the threshold value, the iteration termination condition is not satisfied, and the iteration is continued, including obtaining a better population through neighborhood search, performing genetic operation by using the better population in the fitness model, calculating the fitness of the current model, comparing the fitness with the previous fitness, and judging whether to continue iteration through neighborhood search or judging whether to satisfy the test termination condition according to the comparison result.
Preferably, the mutation test generation method is applied to a vehicle function safety test.
The invention provides a system capable of executing the mutation test generation method, which comprises the following steps:
The analysis module is used for carrying out module architecture analysis on the tested functional module to obtain a module architecture analysis result;
the coding module is used for coding the module architecture analysis result obtained by the analysis module to obtain a primary variation test case population;
The construction module is used for constructing a control flow constraint function, a data flow constraint function and a signal flow constraint according to the module architecture analysis result obtained by the analysis module, and then carrying out weighted coupling on the control flow constraint function, the data flow constraint function and the signal flow constraint function to obtain the fitness model;
The judging iteration module is used for calculating the fitness of the fitness model constructed by the construction module and judging whether the test termination condition is met or not according to the fitness; if not, iterating the fitness model, judging whether the iteration termination condition is met according to the change of the fitness, if so, outputting a variation result, otherwise, continuing the iteration; specifically, the adaptability of the adaptability function is calculated by adopting the primary variation test case population obtained by the coding module and adopting a non-dominant sorting algorithm, and the adaptability is replaced by 1; judging whether the calculated current fitness model meets the test termination condition or not, wherein the method comprises the steps of iterating the first generation mutation test case population, and performing genetic operation on the fitness model according to the iterated mutation test case population to obtain fitness; comparing the fitness with the previous fitness, if the comparison result is smaller than a threshold value, continuing iteration without meeting the iteration termination condition, otherwise, judging whether the test termination condition is met to score the current mutation test, and putting the result into a mutation test case library;
The construction module comprises a control flow constraint function construction unit, a data flow constraint function construction unit, a signal flow constraint function construction unit and an adaptability model construction unit;
The control flow constraint function construction unit is used for performing control flow statement approximate analysis according to the module architecture analysis result obtained by the analysis module, and constructing the control flow constraint function according to the analysis result, and comprises the following steps: deleting a dead basic block in the module architecture analysis result, and scanning a triple-address code and the basic block in the module architecture analysis result to obtain the program control flow graph; constructing the control flow constraint function according to the program control flow graph, taking the farthest path which accords with expected execution as control flow fitness, and obtaining control flow constraint in the control flow constraint function according to the control flow fitness;
The data flow constraint function construction unit is used for carrying out data flow similarity analysis according to the module architecture analysis result obtained by the analysis module, specifically, traversing the module architecture analysis result after optimizing to obtain the program data flow graph; determining the data flow fitness according to the program data flow graph; obtaining the data flow constraint in the data flow constraint function according to the data flow adaptability; the data flow fitness is the path of a variable and the boundary coverage of data, and the data refers to the dimension covered by the variable value change; determining data flow fitness according to a similarity analysis result, and constructing the data flow constraint function, wherein the data flow constraint function is in the range of an obtained program data flow graph; wherein the sum of the data flow constraint function and the data flow constraint is 1;
The signal flow constraint function construction unit is used for scanning input and output parameters in the module architecture analysis result obtained by the analysis module to obtain a program signal flow diagram; constructing the signal flow constraint function according to the signal flow adaptability; constructing a signal threshold envelope constraint boundary table according to the signal flow fitness, and taking the signal threshold envelope constraint boundary table as the constraint of the signal flow constraint function, wherein the signal flow constraint function is in the range of the program signal flow diagram; the signal stream fitness is the maximum boundary that is desirable for execution.
The fitness model construction unit is used for constructing a fitness model after weighting and coupling the control flow constraint function constructed by the control flow constraint function construction unit, the data flow constraint function constructed by the data flow constraint function construction unit and the signal flow constraint function constructed by the signal flow constraint function construction unit.
The invention provides a computer storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the mutation test generation method and the mutation test generation method described by the system.
The technical scheme of the invention provides a variation test generation method, a variation test generation system and a computer storage medium, wherein the variation test generation method comprises the following steps: constructing a control flow constraint function, a data flow constraint function and a signal flow constraint according to the acquired module architecture analysis result, and then constructing an adaptability model; and calculating the fitness of the fitness model by adopting a non-dominant sorting method, and judging whether the test is ended or iterated according to the fitness. In the iteration, genetic operation is carried out on the fitness model, whether the test termination condition is met is judged according to the two adjacent generations of fitness changes, if so, a variation result is output, and otherwise, iteration is continued.
The invention has the advantages that: based on the traditional control flow constraint, the method for reconstructing a new fitness model by combining the data flow constraint and the signal flow constraint improves the mutation score in an evaluation system of the quality measurement of the test case, wherein the comprehensive quality measurement such as coverage, accuracy and reliability is improved by 10%. And by combining signal flow constraint to construct an adaptability model, the misjudgment rate of all module function test cases of 88+40 misaction scenes of the variation test on the automobile safety airbag standard is reduced (below 2%). And when the signal flow constraint function is processed, adding a threshold line envelope generated by fitting analysis such as maximum likelihood estimation and the like, and improving the accuracy of the interactive signal boundary of the variant test case design. And carrying out genetic operation on the fitness model, and using the change amount of the fitness of two adjacent generations as a threshold value, so as to intervene in suppressing population iteration, effectively avoid premature genetic algorithm and reduce the iteration times (about 16 percent).
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, a brief description will be given below of the drawings required for the embodiments or the prior art descriptions, and it is obvious that the drawings in the following description are some embodiments of the present invention, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of an embodiment of a mutation test generation method provided by the invention.
FIG. 2 is a detailed flowchart of an embodiment of a mutation test generation method according to the present invention.
FIG. 3 is a schematic structural diagram of a mutation test generation system according to the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
First, a brief description of the implementation process of the present invention is provided, and fig. 1 is a schematic flow chart provided in an embodiment of the present invention, where fig. 1 includes:
and 101, carrying out module architecture analysis on the tested functional module to obtain a module architecture analysis result.
The step is used for analyzing the abstract grammar tree in the tested function module, and is used for subsequent compiling and processing according to the three-address code, the basic style and the dead basic block obtained from the abstract grammar tree.
And 102, generating a first-generation mutation test case population.
And (3) coding a module architecture analysis result to generate a first-generation variation test case population for carrying out genetic operation by constructing a fitness model later.
And 103, performing control flow statement approximation analysis to construct a control flow constraint function.
Specifically, performing control flow statement approximate analysis on the module architecture analysis result includes deleting a dead basic block in the module architecture analysis result, and scanning a three address code and the basic block in the dead basic block, so that a program control flow diagram is obtained through approximate analysis. And taking the most original path which accords with expected execution as control flow fitness and taking the control flow fitness as control flow constraint of a control flow constraint function.
Thereby constructing a control flow restriction function according to the control flow fitness. The constructed control flow restriction function is within the scope of the program control flow graph.
And 104, carrying out data stream similarity analysis and constructing a data stream constraint function.
Specifically, deleting a dead basic block in the analysis result of the module architecture, compiling the deleted data, and traversing to obtain a program data flow diagram. And taking the path of the variable and the boundary coverage of the data as data flow fitness, and carrying out similarity analysis on the data flow fitness to obtain data flow constraint, so that a data flow constraint function is obtained, wherein the data flow constraint function is in the range of a program data flow graph.
Wherein the sum of both the data flow constraint function and the data flow constraint is 1. In the boundary of the data, the data refers to the dimension covered by the change of the data index variable value, and the boundary refers to the critical line of the reachable dimension.
And 105, carrying out signal threshold envelope analysis and constructing a signal flow constraint function.
Specifically, input and output parameters in the analysis result of the module architecture are linearly scanned, and a program signal flow diagram is obtained. The maximum boundary that is expected to perform is taken as the signal stream fitness. And constructing a signal threshold envelope constraint boundary table according to the signal flow fitness, and taking the table as a signal flow constraint, thereby constructing a signal flow constraint function. The constructed signal flow constraint function is within the range of the sequence signal flow graph.
And 106, constructing an adaptability model, and calculating the adaptability.
And (3) performing weighted coupling on the control flow constraint function, the data flow constraint function and the signal flow constraint function obtained in the step (103), the step (104) and the step (105) to obtain an adaptability model. And (3) bringing the first-generation variation test case population into an fitness model, and calculating the fitness of the newly constructed fitness model by adopting a non-dominant sorting algorithm, wherein the generation is 1.
Step 107, judging whether the fitness meets the test termination condition, if yes, executing step 108, otherwise executing step 109 to enter iteration.
Judging whether the fitness meets the termination condition, if so, executing the step 108, otherwise, executing the step 109.
And step 108, scoring the current mutation test, and putting the result into a mutation test case library.
And step 109, the fitness model executes genetic operation, judges whether the fitness of two adjacent generations is smaller than a threshold value, if yes, executes step 110, otherwise, executes step 108.
And (3) screening and iterating the first-generation variation test case population through genetic operation, calculating the fitness again by the fitness model according to an iteration result, comparing the fitness with the previous fitness, judging whether the fitness of two adjacent generations is larger than a threshold value, if the comparison result is larger than the threshold value, satisfying a termination condition, executing step 107, otherwise, executing step 110.
Step 110, performing neighborhood searching.
After the field search is performed to obtain the preferred population that can be used in the next round, the non-dominant ranking is used again to calculate the current fitness, and then the current fitness is compared with the previous fitness value obtained in step 109.
The technical scheme of the present invention will now be described in detail with reference to a specific embodiment, as shown in fig. 2.
It should be noted that, in the technical solution of the present invention, a dead basic block is a basic block with an unreachable entry, for example, debug code which is not annotated but does not play a role in the development process.
Step 201, performing module architecture analysis on the tested functional module.
The method comprises the step of analyzing an abstract grammar tree of a tested function module to obtain a module architecture analysis result which can be used for subsequent compiling and processing, such as a compiled three-address code, a basic block, a dead basic block and the like.
And 202, generating a first-generation mutation test case population.
And (3) encoding the module architecture analysis result obtained in the step (201) to obtain a primary variation test case population.
Step 203, acquiring a program control flow graph.
Including optimizing the dead basic block from affecting the control flow graph, thereby generating a large number of useless machine code pollution follow-up algorithms. And linearly scanning the three address codes and the control flow basic blocks to obtain a program control flow graph.
And 204, performing approximation analysis on the control flow statement to construct a control flow constraint function.
Re-analyzing the program control flow graph, taking the farthest path which accords with expected execution as a control flow target branch, namely the control flow fitness (Control Flow Fitness function, cff). Further data processing for control flow adaptation, including but not limited to using a function such as the distance from the test branch to the target branch or the hamming distance that serves the purpose of detecting deviations in the control process, as a control flow constraint normalize (device (X)).
Thereby obtaining a control flow constraint function (Control Flow Constrained function, cfc):
cfc(X)＝nomalize(deviation(X))
The control flow constraint function cfc is built within the scope of the program control flow graph.
Step 205, acquiring a program data flow graph.
Optimizing a dead basic block in a data stream statement, and traversing a compiled three address code and a control stream basic block in the basic block to obtain a program data flow diagram. And in the traversal process, the depth traversal is prioritized, the front-back mapping relation of the data in use is acquired, and the similarity analysis is convenient. The program data flow graph can be acquired by traversing the program control flow graph.
And 206, analyzing the similarity of the data stream sentences, and constructing a data stream constraint function.
And acquiring the data stream fitness according to the program data flow graph, and specifically, taking the path of the variable and the boundary coverage of the data as the data stream target value, namely the data stream fitness. Variables are defined for variables, and paths of variables are dimensions of variables reachable in the data flow graph (equivalent to dimensions of basic blocks reachable in the control flow graph); data refers to the dimension over which a variable value varies, and the boundaries of data refer to the range over which the variable value can vary. The data table consisting of these two dimensions is the data stream fitness (Data Flow Fitness function, dff).
A data stream constraint distance (X) is obtained according to the data stream fitness, including but not limited to the distance from a test value to a target value in a data matrix or Manhattan distance. Data flow constraint function (Data Flow Constrained function, dfc) =1-data flow constraint.
dfc(X)＝1-distance(X)
When the data flow constraint function is processed in the step, similarity analysis is added, so that the misjudgment rate of the air bag misaction test data test set is reduced, and flexible control of the variable weight of the data set in the fitness function is realized.
Step 207, acquiring a program signal flow diagram.
And linearly scanning the input and output parameters in the analysis result of the module architecture to obtain a program signal flow diagram.
And step 208, carrying out signal threshold envelope analysis and constructing a signal flow constraint function.
The program signal flow graph is subjected to signal threshold envelope analysis, and the maximum boundary which accords with expected execution is taken as a control flow target branch, namely signal flow fitness (SIGNAL FITNESS function, sf). And obtaining a signal threshold envelope constraint boundary table range (X) according to a method for obtaining the similarity of the signal flow such as the distance from the test branch to the target branch or Jaccard coefficient and the like, thereby constructing a signal flow constraint function sfc (X).
sfc(X)＝1-range(X)
Wherein the signal flow restriction function is within the scope of the program signal flow graph. The method comprises the steps of adding a signal threshold envelope constraint boundary table generated by fitting analysis such as maximum likelihood estimation and the like when a signal flow constraint function is constructed, fitting a signal threshold line which possibly appears in real signal transmission, forming a closed loop for control flow when multiple-core redundancy, exception handling and other use cases are generated, improving the validity of signals and interface boundary values when variation test cases are generated under different pulse signal inputs, and finally improving the validity of the generated variation test cases.
And 209, constructing an adaptability model.
Constructing a fitness model fitness (X) according to the control flow constraint function, the data flow constraint function and the signal flow constraint function weighted coupling constructed in the steps 204, 206 and 208:
fitness(X)＝a*cfc(X)+b*dfc(X)+c*range(X)
wherein a, b, c are the weight coefficients of the control stream constraint function, the data stream constraint function, and the signal stream constraint function, respectively, a+b+c=1, (a, b, c e (0, 1)).
Compared with the traditional variation test method, the method increases triple constraint weighted coupling of control flow constraint, data flow constraint and signal flow constraint, and builds a new fitness function. Therefore, the traditional single-target genetic algorithm generation is improved to a multi-target genetic algorithm, the design flexibility is improved, and meanwhile, the test case variation score can be effectively improved.
At step 210, fitness of the fitness model is calculated.
And adopting a non-dominant ranking genetic algorithm, assuming generation Gen=1, and adopting the primary mutation case population obtained in the step 202 to calculate the fitness.
Step 211, judging whether the test termination condition is satisfied.
Specifically, it is determined whether the fitness obtained in step 210 meets a termination test condition, where the termination condition is whether the current optimal solution exceeds the expected or the iteration reaches the specified maximum evolution algebra GEN, if the termination condition is met, step 212 is executed, otherwise, the iteration is entered.
And 212, grading, and putting the adopted population into a variation test case library.
Specifically, the fitness obtained in step 210 is decoded and scored, and the variant test case population adopted in step 210 is placed in a variant test case library.
If iteration is entered, a loop from step 213 to step 216 is executed, the fitness model executes genetic operation, judges whether the fitness of two adjacent generations is larger than a threshold value, judges whether to iterate again by adopting neighborhood search according to the comparison result, and is used for realizing convergence of the multi-objective optimization algorithm to a local optimal solution:
And 213, screening the population to obtain a new variation test case population.
Algebraic addition 1, gen=gen+1, and after selection/crossover/mutation is performed on the previous population, the repeated individuals are removed after the parent-child populations are combined, and a new mutation test case population is obtained.
Step 214, calculating new fitness.
Substituting the new variation test case population into the fitness model, and calculating by adopting a non-dominant sorting algorithm to obtain new fitness.
Step 215, comparing, and judging whether the difference value of the fitness difference is smaller than a threshold value.
Comparing the new fitness with the previous fitness, determining whether the fitness difference (Δfitness) is smaller than a threshold, if yes, executing step 216, otherwise returning to step 211. The iteration termination condition is that the difference value of the two adjacent generations of fitness is not smaller than a threshold value, if the iteration termination condition is met, returning to step 211, entering the main flow press to meet the test termination condition, and if not, re-entering the iteration. Therefore, the method uses the change quantity of the adaptation degree of two adjacent generations as a threshold value, and intervenes and inhibits population iteration by taking the threshold value as a screening condition, so that the aim of reducing iteration stagnation of a multi-objective optimization algorithm is fulfilled.
Step 216, performing neighborhood searching.
The fitness is calculated by searching for a more optimal population through neighborhood searching, returning to step 214 after obtaining a more optimal population.
It should be noted that, after the step 216 is performed to obtain a better population, the population iteration of the step 213 is temporarily suspended, and if the fitness difference value after the recalculation is greater than the threshold value and the termination condition is not satisfied, the step 213 continues the population iteration.
The mutation test generation method provided by the invention is preferably applied to vehicle function safety tests, wherein the safety tests comprise but are not limited to: the safety of the vehicle-mounted system is tested, and the safety test system comprises whether an airbag, an anti-lock brake system, a vehicle body stability control system and the like can work normally, and the test direction of the vehicle-mounted system for response and treatment of collision and emergency conditions and the like.
As shown in fig. 3, the present invention provides a system capable of executing the mutation test generation method, including:
the analysis module 301 is configured to perform module architecture analysis on the tested functional module to obtain a module architecture analysis result.
The encoding module 302 is configured to encode the module architecture analysis result obtained by the analysis module 301 to obtain a population of primary variation test cases.
The construction module 303 is configured to construct a control flow constraint function, construct a data flow constraint function, and construct a signal flow constraint according to the module architecture analysis result obtained by the analysis module 301, and then perform weighted coupling on the control flow constraint function, the data flow constraint function, and the signal flow constraint function to obtain an fitness model.
The judging iteration module 304 is configured to calculate the fitness of the fitness model constructed by the construction module 303, and judge whether the test termination condition is satisfied according to the fitness; if not, iterating the fitness model, judging whether the iteration termination condition is met according to the change of the fitness, if so, outputting a variation result, otherwise, continuing the iteration; specifically, the fitness of the fitness function is calculated by adopting a non-dominant sorting algorithm by adopting the primary variation test case population obtained by the encoding module 302, and the code is 1; judging whether the calculated current fitness model meets the test termination condition or not, wherein the method comprises the steps of iterating a first-generation variation test case population, and performing genetic operation according to the iterated variation test case population by the fitness model to obtain fitness; and comparing the fitness with the previous fitness, if the comparison result is smaller than the threshold value, continuing iteration without meeting the iteration termination condition, otherwise, judging whether the test termination condition is met, scoring the current mutation test, and putting the result into a mutation test case library.
The building module 303 includes a control flow constraint function building unit 3031, a data flow constraint function building unit 3032, a signal flow constraint function building unit 3033, and an fitness model building unit 3034.
The control flow constraint function construction unit 3031 is configured to perform control flow statement approximation analysis according to a module architecture analysis result obtained by the analysis module 301, and construct the control flow constraint function according to the analysis result, where the control flow constraint function comprises: deleting a dead basic block in the module architecture analysis result, and scanning a three-address code and the basic block in the module architecture analysis result to obtain the program control flow diagram; and constructing a control flow constraint function according to the program control flow graph, taking the farthest path which accords with expected execution as control flow fitness, and obtaining control flow constraint in the control flow constraint function according to the control flow fitness.
The data flow constraint function construction unit 3032 is configured to perform data flow similarity analysis according to a module architecture analysis result obtained by the analysis module 301, specifically, perform traversal after optimizing the module architecture analysis result, so as to obtain a program data flow graph; determining the data flow fitness according to the program data flow graph; obtaining data flow constraint in a data flow constraint function according to the data flow adaptability; the data flow fitness is the variable path and the data boundary coverage, and the data refers to the variable value change coverage dimension; determining data flow fitness according to a similarity analysis result, and constructing a data flow constraint function, wherein the data flow constraint function is in the range of an obtained program data flow graph; wherein the sum of the data flow constraint function and the data flow constraint is 1.
The signal flow constraint function construction unit 3033 is configured to scan input and output parameters in the analysis result of the module architecture obtained by the analysis module 301, so as to obtain a program signal flow diagram; constructing a signal flow constraint function according to the signal flow adaptability; constructing a signal threshold envelope constraint boundary table according to the signal flow fitness, and taking the signal threshold envelope constraint boundary table as the constraint of the signal flow constraint function, wherein the signal flow constraint function is in the range of the program signal flow diagram; the signal stream fitness is the maximum boundary that is desirable for execution.
The fitness model building unit 3034 is configured to weight-couple the control flow constraint function built by the control flow constraint function building unit 3031, the data flow constraint function built by the data flow constraint function building unit 3032, and the signal flow constraint function built by the signal flow constraint function building unit 3033 to build a fitness model.
The invention provides a computer storage medium, on which a computer program is stored, characterized in that the computer program, when executed by a processor, implements the steps of the mutation test generation method and the mutation test generation method described by the system.
Those skilled in the art will appreciate that implementing all or part of the above-described methods may be accomplished by way of a computer program stored on a computer readable storage medium, which when executed, may comprise the steps of the embodiments of the methods described above. Any reference to memory, storage, database, or other medium used in embodiments provided herein may include non-volatile and/or volatile memory. The nonvolatile memory can include Read Only Memory (ROM), programmable ROM (PROM), electrically Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), or flash memory. Volatile memory can include Random Access Memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms such as Static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double Data Rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (SYNCHLINK) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), among others.
The adaptability model constructed by the control flow constraint function, the data flow function and the signal flow constraint function can be iterated by adopting a multi-objective genetic algorithm, so that the problem of generating a variation test case is solved, and the problems that in the prior art, the variation test object is single, the test case is low in effectiveness, the data flow existing in a real scene cannot be constrained, the constraint relation cannot be established between the judging condition and the expression which are dependent on the input variable, and the corresponding expression cannot be generated for testing are also solved.
The method reduces the problem that the multi-objective genetic algorithm converges to the local optimal solution by constructing the fitness function, checking the adjacent fitness substitution difference, and finally using the combination method of the domain search algorithm, and solves the problem of more algorithm iteration times in the existing mutation test.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (18)
1. A mutation test generation method, the method comprising:
Constructing an adaptability model after constructing a control flow constraint function, a data flow constraint function and a signal flow constraint according to the acquired module architecture analysis result;
calculating the fitness of the fitness model, and judging whether a test termination condition is met or not according to the fitness; if not, iterating the fitness model, judging whether the iteration termination condition is met according to the change of the fitness, if so, outputting a variation result, otherwise, continuing the iteration.
2. The mutation test generation method according to claim 1, wherein the module architecture analysis result is encoded to obtain the first generation mutation test case population.
3. The mutation test generation method according to claim 1, wherein the constructing a control flow constraint function includes:
And performing control flow statement approximate analysis on the module architecture analysis result, and constructing the control flow constraint function according to the analysis result.
4. The mutation test generation method according to claim 3, wherein the performing control flow statement approximation analysis on the module architecture analysis result includes:
deleting a dead basic block in the module architecture analysis result, and scanning a triple-address code and the basic block in the module architecture analysis result to obtain the program control flow graph;
Constructing the control flow constraint function according to the program control flow graph;
Wherein the control flow restriction function is within the scope of the program control flow graph.
5. The mutation test generation method according to claim 3, wherein said constructing the control flow constraint function according to the program control flow graph comprises:
And taking the farthest path which accords with expected execution as control flow fitness, and obtaining control flow constraint in the control flow constraint function according to the control flow fitness.
6. The mutation test generation method of claim 1, wherein the constructing a data flow constraint function comprises:
And carrying out data flow similarity analysis on the module architecture analysis result, determining data flow fitness according to the analysis result, and constructing the data flow constraint function, wherein the data flow constraint function is in the range of the obtained program data flow graph.
7. The mutation test generation method according to claim 6, wherein the performing data flow similarity analysis on the module architecture analysis result includes:
traversing the module architecture analysis result after optimizing to obtain the program data flow graph; determining the data flow fitness according to the program data flow graph; obtaining the data flow constraint in the data flow constraint function according to the data flow adaptability;
The data flow fitness is the path of a variable and the boundary coverage of data, and the data refers to the dimension covered by the variable value change.
8. The mutation test generation method of claim 7, wherein a sum of the data flow constraint function and the data flow constraint is 1.
9. The mutation test generation method of claim 1, wherein the constructing a signal flow constraint function comprises:
scanning input and output parameters in the module architecture analysis result to obtain a program signal flow diagram;
constructing the signal flow constraint function according to the signal flow adaptability;
Wherein the signal flow restriction function is within the program signal flow graph.
10. The mutation test generation method according to claim 9, wherein a signal threshold envelope constraint boundary table is constructed according to the signal stream fitness, and the signal threshold envelope constraint boundary table is used as a constraint of the signal stream constraint function.
11. The mutation test generation method of claim 9, wherein the signal stream fitness is a maximum boundary that is desirable for execution.
12. The mutation test generation method according to claim 1, wherein the constructing the fitness model includes: and performing weighted coupling on the control flow constraint function, the data flow constraint function and the signal flow constraint function to obtain the fitness model.
13. The mutation test generation method according to claim 2, wherein the calculating the fitness of the fitness model, and determining whether a test termination condition is satisfied according to the fitness, comprises:
Calculating the fitness of the fitness function by adopting the primary variation test case population and adopting a non-dominant sorting algorithm, substituting 1, judging whether the current fitness model meets the test termination condition, scoring the current variation test if yes, and putting the result into a variation test case library; otherwise, the iteration is entered.
14. The mutation test generation method according to claim 2, wherein determining whether an iteration termination condition is satisfied according to the change in the fitness comprises:
Iterating the first generation mutation test case population, and performing genetic operation by the fitness model according to the iterated mutation test case population to obtain fitness;
And comparing the fitness with the previous fitness, if the comparison result is smaller than a threshold value, continuing iteration without meeting the iteration termination condition, otherwise, judging whether the test termination condition is met.
15. The mutation test generation method of claim 14, wherein continuing the iteration without satisfying the iteration termination condition if the comparison result is less than a threshold value comprises:
Obtaining a better population through neighborhood search, performing genetic operation by the adaptation degree model by adopting the better population, calculating the adaptation degree of the current model, comparing the adaptation degree with the previous adaptation degree, and judging whether iteration is continued through the neighborhood search or whether the test termination condition is met according to a comparison result.
16. The mutation test generation method according to any one of claims 1-15, wherein the mutation test generation method is applied to a vehicle function safety test.
17. A mutation test generation system capable of performing the method of any one of claims 1-16, comprising:
The analysis module is used for carrying out module architecture analysis on the tested functional module to obtain a module architecture analysis result;
the coding module is used for coding the module architecture analysis result obtained by the analysis module to obtain a primary variation test case population;
The construction module is used for constructing a control flow constraint function, a data flow constraint function and a signal flow constraint according to the module architecture analysis result obtained by the analysis module, and then carrying out weighted coupling on the control flow constraint function, the data flow constraint function and the signal flow constraint function to obtain the fitness model;
The judging iteration module is used for calculating the fitness of the fitness model constructed by the construction module and judging whether the test termination condition is met or not according to the fitness; if not, iterating the fitness model, judging whether the iteration termination condition is met according to the change of the fitness, if so, outputting a variation result, otherwise, continuing the iteration; specifically, the adaptability of the adaptability function is calculated by adopting the primary variation test case population obtained by the coding module and adopting a non-dominant sorting algorithm, and the adaptability is replaced by 1; judging whether the calculated current fitness model meets the test termination condition or not, wherein the method comprises the steps of iterating the first generation mutation test case population, and performing genetic operation on the fitness model according to the iterated mutation test case population to obtain fitness; comparing the fitness with the previous fitness, if the comparison result is smaller than a threshold value, continuing iteration without meeting the iteration termination condition, otherwise, judging whether the test termination condition is met to score the current mutation test, and putting the result into a mutation test case library;
The construction module comprises a control flow constraint function construction unit, a data flow constraint function construction unit, a signal flow constraint function construction unit and an adaptability model construction unit;
The control flow constraint function construction unit is used for performing control flow statement approximate analysis according to the module architecture analysis result obtained by the analysis module, and constructing the control flow constraint function according to the analysis result, and comprises the following steps: deleting a dead basic block in the module architecture analysis result, and scanning a triple-address code and the basic block in the module architecture analysis result to obtain the program control flow graph; constructing the control flow constraint function according to the program control flow graph, taking the farthest path which accords with expected execution as control flow fitness, and obtaining control flow constraint in the control flow constraint function according to the control flow fitness;
The data flow constraint function construction unit is used for carrying out data flow similarity analysis according to the module architecture analysis result obtained by the analysis module, specifically, traversing the module architecture analysis result after optimizing to obtain the program data flow graph; determining the data flow fitness according to the program data flow graph; obtaining the data flow constraint in the data flow constraint function according to the data flow adaptability; the data flow fitness is the path of a variable and the boundary coverage of data, and the data refers to the dimension covered by the variable value change; determining data flow fitness according to a similarity analysis result, and constructing the data flow constraint function, wherein the data flow constraint function is in the range of an obtained program data flow graph; wherein the sum of the data flow constraint function and the data flow constraint is 1;
The signal flow constraint function construction unit is used for scanning input and output parameters in the module architecture analysis result obtained by the analysis module to obtain a program signal flow diagram; constructing the signal flow constraint function according to the signal flow adaptability; constructing a signal threshold envelope constraint boundary table according to the signal flow fitness, and taking the signal threshold envelope constraint boundary table as the constraint of the signal flow constraint function, wherein the signal flow constraint function is in the range of the program signal flow diagram; the signal flow fitness is the maximum boundary which accords with expected execution;
The fitness model building unit is used for building the fitness model after weighting and coupling the control flow constraint function built by the control flow constraint function building unit, the data flow constraint function built by the data flow constraint function building unit and the signal flow constraint function built by the signal flow constraint function building unit.
18. A computer storage medium having stored thereon a computer program, wherein the computer program when executed by a processor implements the steps of the mutation test generation method of any of claims 1 to 16.
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