CN111209192A - Test case automatic generation method based on double-chaos whale optimization algorithm - Google Patents
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Abstract
The invention discloses a test case automatic generation method based on a double-chaos whale optimization algorithm, which belongs to the field of computer test and specifically comprises the following steps: acquiring a tested program, obtaining the number of input parameters, analyzing the tested program, converting the tested program into a control flow diagram and performing program instrumentation; calculating a test path set of the tested program according to the control flow graph, and initializing the population by using the chaotic sequence to replace a random algorithm in a whale algorithm; obtaining a fitness function according to a layer proximity method and a branch distance method; optimizing the population aiming at one test path each time, performing chaotic disturbance operation after an optimal value is obtained to obtain a new initialized population, performing whale optimization algorithm again, and repeating iteration to obtain a group of test cases; repeating the steps to obtain a complete test case set. According to the invention, the whale optimization algorithm is applied to the test case generation, and the chaos strategy is used for the algorithm twice, so that the test case generation efficiency is improved.
Description
Technical Field
The invention belongs to the field of computer testing, and particularly relates to a test case automatic generation method based on a double-chaos whale optimization algorithm.
Background
Software testing based on path coverage is a software testing method which is commonly used at present, and the automatic generation of test cases plays a key role in improving the efficiency of software testing. In recent years, researchers at home and abroad continuously combine automatic test case generation with various meta-heuristic optimization algorithms, and the time and quality of test case generation are greatly improved. Genetic algorithms and particle swarm algorithms are commonly used at present, such as an automatic test case generation method based on an output domain proposed by Yougueng et al and a particle swarm optimization test case generation method based on pattern combination proposed by ginger fair et al. Genetic algorithms have been commonly applied in automatic test case generation based on path coverage, but there are two problems: firstly, when searching for an optimal solution, it is difficult to ensure that the algorithm can converge to a global optimal solution; second, there is no efficient fitness function to support the selection of test data. Because the particle swarm algorithm has a simple structure and high optimization speed, researchers introduce the particle swarm algorithm into the generation of test cases, and prove that the particle swarm algorithm is really more effective than a genetic algorithm. However, because the particle swarm algorithm has many parameters, the convergence stability of the later algorithm is seriously influenced by parameter control. Whether the global optimal solution can be converged or not and the convergence speed become key problems which need to be solved at present.
Disclosure of Invention
The invention aims to provide a test case automatic generation method based on a double-chaos whale optimization algorithm aiming at the defects of the conventional meta-heuristic algorithm, solves the problem that the meta-heuristic algorithm is easy to fall into a local optimal solution, and improves the algorithm convergence speed.
In order to achieve the purpose, the invention adopts the following technical scheme:
a test case automatic generation method based on a double-chaos whale optimization algorithm specifically comprises the following steps:
step 1: acquiring a tested program, analyzing the tested program to obtain the number k of input parameters, and converting the tested program into a control flow graph;
step 2: inserting piles into the tested program;
and step 3: calculating the PAT piece of the tested program as { path ═ path }1，path2，...，pathn}; wherein n represents the total number of test paths;
and 4, step 4: initializing population number NP, maximum iteration number Maxgen and optimal value Spath(ii) a Initializing the particles by using Logistic mapping to generate a chaotic sequence to obtain a whale individual set S ═ S i1,2,. NP }, where each individual whale SiPosition vector (x) capable of being expressed in k dimensions1，x2，...，xk)；
And 5: obtaining a fitness function by using a layer proximity method and a branch distance method;
step 6: selecting a PATH from the test PATH set PATHjAs a target test path; j represents the jth test path, and the value of j is from 1 to n;
and 7: running the code after the pile insertion to obtain identification variable and branch distance information;
and 8: calculating the fitness value of the individual according to the fitness function, and finding out the optimal individual S of the current iterationiter；
And step 9: if the current iteration is the optimal individual SiterOptimal value S over target test pathpathThen the optimum value S is updatedpathOtherwise, directly executing the step 10;
step 10: if the optimal value SpathCovered target PathjOr if the iteration times reach the maximum iteration times Maxgen, jumping to the step 12, otherwise, jumping to the step 11;
step 11: for the optimal value SpathPerforming chaotic disturbance operation to obtain optimal individual S from chaotic sequence*(ii) a Respectively with SpathAnd S*Taking NP/2 as the population number as the center to form a new population, taking the new population as a new input, and skipping to the step 7;
step 12: stopping population evolution, and obtaining the optimal value SpathStoring the test case set A; adding j to 1, if j is larger than n, skipping to stepStep 13, otherwise, setting the optimal value to be null, taking the initialized individual in the step 4 as a new initialized population, and testing the PATH in the PATH set PATHjAs a new target test path and jumping to step 7;
step 13: the loop is terminated and test case set a is output.
Preferably, in step 5:
the layer proximity method refers to the actual test path and the target test pathjThe degree of closeness of (2), the fitness value calculation formula of the layer approach method is shown in (1):
wherein layer _ method (S)i) Represents whale individual SiThe fitness value of the layer proximity method is shown, u represents the total number of branches of the test path, and u represents the number of branches of the actual test path, which are different from the target test path;
the branch distance method refers to the distance between an actual test path branch and a target test path branch, and when the distance is 0, it indicates that the branch is covered; the fitness value calculation formula of the branch distance method is shown in (2):
wherein layer _ method (S)i) Represents whale individual SiFitness value dis of the branch distance methodmIndicating the present whale individual SiDistance from the mth branch;
the form of the fitness function is shown in equation (3):
fitness(Si)＝layer_method(Si)+branch_method(Si) (3)；
wherein layer _ method (S)i) Represents whale individual SiFitness value of layer proximity method, layer _ method (S)i) Represents whale individual SiFitness value of the branch distance method.
The invention has the following beneficial technical effects:
random algorithms are mostly adopted for automatic generation of the existing test cases, researchers mainly research improvement of particle swarm optimization algorithms, no new attempt is made to utilize some novel heuristic optimization algorithms to automatically generate the test cases, and the whale optimization algorithm is adopted for automatic generation of the test cases for the first time. Firstly initializing whale populations, then executing a whale optimization algorithm according to the fitness value, and finally obtaining whale individuals with the minimum fitness value, namely the optimal test cases. The whale optimization algorithm has two advantages, one is that the algorithm is simple in structure, small in calculation amount and simple in parameters, parameter control is facilitated, and the problem of parameter control caused by a large number of parameters in the particle swarm optimization can be solved; secondly, the searching mechanism of the algorithm is unique, and the positions of individuals are updated respectively in two modes of randomly selecting a contraction ring and a spiral bubble net with the probability of 50 percent, so that the possibility of jumping out of the local optimal solution is provided; the method integrates simple structure and small calculation amount of whale optimization algorithm, not only solves the problem that the random algorithm is too simple, but also solves the problem that the parameter control of the particle swarm is difficult; secondly, the coverage criterion adopted by the method is a path coverage criterion, and the method has higher error correction capability;
the method combines and uses two chaotic operations when a whale optimization algorithm is carried out. Firstly, when a whale fingerling group is initialized by a whale optimization algorithm, particles initialized by a random algorithm are not uniformly dispersed and have poor diversity, and an initialized population is generated by using Logistic mapping, so that the particles are more uniformly distributed, the particle diversity is improved, the particles are prevented from falling into a local optimal solution too early, and the convergence speed of the algorithm is improved; secondly, obtaining the optimal value S of the target test path in the current iterationpathThen, the chaotic signal is subjected to mixed disturbance operation to obtain an optimal value S from the chaotic sequence*And then, initializing individual populations with NP/2 number by taking the two optimal values as centers to form a new initialized population, optimizing the whale optimization algorithm again, jumping out of the local optimal when the particles are trapped in the local optimal solution, and converging to the global optimal, thereby improving the generation efficiency of the test case.
Drawings
FIG. 1 is a diagram of a triangle classifier.
FIG. 2 is a flow chart of a whale optimization algorithm.
Fig. 3 is a control flow diagram of triangle classification.
Fig. 4 is a graph of the experimental results.
Detailed Description
The invention is described in further detail below with reference to the following figures and detailed description:
the triangle classification program shown in fig. 1 is taken as an example in the experiment, and the whole process of the automatic generation of the test case shown in fig. 2 is verified, which includes the following steps:
and (3) experimental environment configuration: windows10 operating system, matlabR2018b, C language, lcov 1.13.
Inputting: number of population NP, maximum number of iterations Maxgen, population dimension k, initial population position S ═ Si|i＝1，2，...NP}。
And (3) outputting: test case set a.
Step 1: acquiring a tested program, wherein the number k of input parameters of the triangle classification program is 3, and the number k is (a, b, c), and converting the tested program into a control flow shown in fig. 3.
Step 2: program instrumentation refers to instrumentation and compilation of the instrumented program using the gcc compiler to obtain the executable file needed in step 7.
And step 3: and counting a test PATH set PATH of the tested program according to the control flow graph.
The triangle classification procedure comprises 2 multi-branch selection relations, 4 parallel selection relations, and n-19 feasible paths, for example, path1＝{111100}，path2＝{1n1100}，path311n 100. The above information is arranged in a table form as shown in table 2.
TABLE 2 information of the function to be tested
And 4, step 4: initializing population number NP, maximum iteration number Maxgen and optimal value Spath(ii) a When the whale optimization algorithm is initialized, the whale optimization algorithm is initialized by using Logistic mapping to generate a chaotic sequence, NP whale individuals can be obtained, and the position vector of each whale individual is S ═ Si|i＝1，2，...50}。
And 5: obtaining a fitness function by using a layer proximity method and a branch distance method;
the layer proximity method refers to the actual test path and the target test pathjThe degree of closeness of (2), the fitness value calculation formula of the layer approach method is shown in (1):
wherein layer _ method (S)i) Represents whale individual SiThe fitness value of the layer proximity method is shown, u represents the total number of branches of the test path, and u represents the number of branches of the actual test path, which are different from the target test path;
the branch distance method refers to the distance between an actual test path branch and a target test path branch, and when dis is 0, it indicates that the branch is covered; the fitness value calculation formula of the branch distance method is shown in (2):
wherein layer _ method (S)i) Represents whale individual SiFitness value dis of the branch distance methodmIndicating the present whale individual SiDistance from the mth branch;
the form of the fitness function is shown in equation (3):
fitness(Si)＝layer_method(Si)+branch_method(Si) (3)；
wherein layer _ method (S)i) Represents whale individual SiFitness value of layer proximity method, layer _ method (S)i) Represents whale individual SiBranch distanceFitness value of the method.
Step 6: sequentially selecting one PATH at a time from the test PATH set PATHjAs a target test path.
And 7: running the triangle classification program after pile insertion to obtain branch distance information; the cover. info file generated by using the lcov tool records the execution times of each branch, and the information of branch execution is converted into a standard identification variable form, so that the identification variable can be obtained.
And 8: calculating the fitness value of each whale individual in each iteration according to the formula (2), finding the whale individual with the minimum fitness value, and recording the whale individual as the current iteration optimal individual Siter。
And step 9: if the current iteration is the optimal individual SiterOptimal value S over target test pathpathThen the optimum value S is updatedpath。
Step 10: optimal value S when whale optimization algorithm is carried outpathThe target test path has been coveredjStopping the whale optimization algorithm of the current target test path; or t is used for representing the current iteration number, and when t is larger than the maximum iteration number Maxgen, the whale optimization algorithm of the current target test path is stopped.
Step 11: to SpathPerforming chaotic disturbance operation to obtain optimal individual S from chaotic sequence*(x1，x2，x3). And respectively centering the two individuals, taking NP/2 as the population number, and entering the whale algorithm optimization stage in the step 7 again.
Step 12: stopping population evolution, and obtaining the optimal value SpathAnd storing the test case into a test case set A. Adding 1 to j, if j is larger than 19, jumping to step 13, otherwise, setting the optimal value to be null, taking the initialized individual in step 4 as a new initialized population, and testing the path in the path setjAs a new target test path and jumps to step 7.
Step 13: the loop is terminated and test case set a is output.
In order to test the advantages and disadvantages of the invention, under the condition of determining the covered target path set, the population size NP is set to 50, and the maximum iteration number Maxgen is set to 1000, the test and the verification can be carried out through three indexes of the coverage rate, the iteration number and the execution time.
TABLE 3 coverage, iteration count and execution time (units: seconds) under the set of target paths to be covered
Table 3 is the average of 100 independent runs for each set of experiments. The improved genetic algorithm is an automatic test case generation method based on an output domain and proposed by Yufeng et al, and the improved particle swarm algorithm is a particle swarm optimization test case generation method based on mode combination and proposed by Zingiber zeugineum et al.
As shown in FIG. 4, in the experiments of the two test procedures of triangle classification and sorting, the improved genetic algorithm, the improved particle swarm algorithm and the traditional whale algorithm are improved and improved to different degrees in comparison with the two indexes of iteration times and execution time.
It is to be understood that the above description is not intended to limit the present invention, and the present invention is not limited to the above examples, and those skilled in the art may make modifications, alterations, additions or substitutions within the spirit and scope of the present invention.
Claims (2)
1. A test case automatic generation method based on a double-chaos whale optimization algorithm is characterized by comprising the following steps:
step 1: acquiring a tested program, analyzing the tested program to obtain the number k of input parameters, and converting the tested program into a control flow graph;
step 2: inserting piles into the tested program;
and step 3: calculating test path set PAT of tested programH＝{path1,path2,...,pathn}; wherein n represents the total number of test paths;
and 4, step 4: initializing population number NP, maximum iteration number Maxgen and optimal value Spath(ii) a Initializing the particles by using Logistic mapping to generate a chaotic sequence to obtain a whale individual set S ═ Si1,2,. NP }, where each individual whale SiPosition vector (x) capable of being expressed in k dimensions1,x2,...,xk)；
And 5: obtaining a fitness function by using a layer proximity method and a branch distance method;
step 6: selecting a PATH from the test PATH set PATHjAs a target test path; j represents the jth test path, and the value of j is from 1 to n;
and 7: running the code after the pile insertion to obtain identification variable and branch distance information;
and 8: calculating the fitness value of the individual according to the fitness function, and finding out the optimal individual S of the current iterationiter；
And step 9: if the current iteration is the optimal individual SiterOptimal value S over target test pathpathThen the optimum value S is updatedpathOtherwise, directly executing the step 10;
step 10: if the optimal value SpathCovered target PathjOr if the iteration times reach the maximum iteration times Maxgen, jumping to the step 12, otherwise, jumping to the step 11;
step 11: for the optimal value SpathPerforming chaotic disturbance operation to obtain optimal individual S from chaotic sequence*(ii) a Respectively with SpathAnd S*Taking NP/2 as the population number as the center to form a new population, taking the new population as a new input, and skipping to the step 7;
step 12: stopping population evolution, and obtaining the optimal value SpathStoring the test case set A; adding j to 1, if j is larger than n, skipping to the step 13, otherwise, setting the optimal value to be null, taking the initialized individual in the step 4 as a new initialized population, and testing the path set PATPath in HjAs a new target test path and jumping to step 7;
step 13: the loop is terminated and test case set a is output.
2. The method for automatically generating the test case based on the bichaotic whale optimization algorithm as claimed in claim 1, wherein in step 5:
the layer proximity method refers to the actual test path and the target test pathjThe degree of closeness of (2), the fitness value calculation formula of the layer approach method is shown in (1):
wherein layer _ method (S)i) Represents whale individual SiThe fitness value of the layer proximity method, u represents the total number of branches of the test path, and v represents the number of branches of the actual test path which are different from the target test path;
the branch distance method refers to the distance between an actual test path branch and a target test path branch, and when the distance is 0, it indicates that the branch is covered; the fitness value calculation formula of the branch distance method is shown in (2):
wherein layer _ method (S)i) Represents whale individual SiFitness value dis of the branch distance methodmIndicating the present whale individual SiDistance from the mth branch;
the form of the fitness function is shown in equation (3):
fitness(Si)＝layer_method(Si)+branch_method(Si) (3)；
wherein layer _ method (S)i) Represents whale individual SiFitness value of layer proximity method, layer _ method (S)i) Represents whale individual SiFitness value of the branch distance method.
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