CN111813669A - Adaptive random test case generation method based on multi-target group intelligence - Google Patents
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Abstract
The method provides a method for generating an adaptive random test case based on multi-target group intelligence, and belongs to an adaptive random test technology in the field of software testing. The basic flow of the method comprises six steps, including calculating the fitness based on the minimum distance maximization, calculating the fitness based on the information entropy of the distance, calculating the fitness based on the sample difference, evolving and iterating, generating and executing the test case, and finally iterating to generate the test case. A simulated comparative experiment was also designed herein for eAR and FSCS-ART. Aiming at the existing adaptive random test method based on evolution and the heuristic adaptive random test method, the test case generated by the invention has higher diversity and stronger failure detection capability.
Description
Technical Field
The invention belongs to an adaptive random test technology in the field of software test, and relates to an adaptive random test case generation method based on multi-target group intelligence.
Background
With the continuous development of the computer industry, the software testing industry is also receiving more and more attention. Particularly, as the software is applied to daily life of people on a large scale, the software quality is directly influenced on the quality of life of people in a certain sense. The most common means for ensuring the software quality is through software testing, so the software testing plays an indispensable and important role in the software development cycle.
Among the many methods of test data generation, the Random Test (RT) algorithm is the simplest and easy to implement algorithm. However, the RT algorithm has a disadvantage in that the algorithm cannot ensure that the test data can be distributed in the input domain "uniformly", so the algorithm often cannot achieve a good detection failure effect. Therefore, many optimization methods are created on the basis of RT, for example, Adaptive Random Test (ART) is proposed by t.y. Chen of the schweiben technical university, ART makes certain limitation on randomness while preserving the randomness of RT (random test method), so that test cases can be distributed in the whole input domain "uniformly", so that the test cases have more diversity, thereby improving the effect of detecting the failure domain.
Many improved algorithms based on heuristic strategies have also been generated on the basis of ART. In 2009, Tappenden added an evolution algorithm to ART for the first time, and proposed an adaptive random test method (eAR) algorithm based on evolution. In conventional ART, test data is often generated by a heuristic strategy. eAR, however, uses meta-heuristic search algorithms to search for better test data throughout the input domain by iterative evolution. In actual life, single-target optimization often cannot solve the problem well, so that a metaheuristic search algorithm based on multi-target optimization, such as NSGA (non-subsampled generalized genetic algorithm), SPEA (spectral analysis), and the like, is generated in the field of group intelligence.
Disclosure of Invention
Aiming at the existing adaptive random test method based on evolution, the invention provides an adaptive random test case generation method based on multi-target group intelligence. In order to solve the defect of single-target optimization, a genetic algorithm is taken as a representative of a group intelligent algorithm on the basis of an eAR algorithm, an algorithm NSGA-II which is popular in the field of multi-target optimization at present is adopted, an original single-target optimization function is converted into a multi-target optimization function, feasible solution is more uniform in input space distribution, finally generated test cases have stronger diversity, and therefore the failure detection capability of the test cases is improved.
The algorithm comprises the following three optimization objective functions: an optimization function based on minimum distance maximization, an optimization function based on information entropy of distance, and an optimization function based on sample difference values. The optimization function based on the minimum distance maximization aims to enable the next test case to be far away from the previously generated test case as far as possible so as to enhance the searching capability of a feasible solution and enable the distribution range of the test case to be wider. The information entropy optimization function based on the distance is used for ensuring that the distance between the next test case and the neighbor is more uniform so as to achieve that each test case is locally uniform as much as possible. The optimization function based on the sample difference value is generated in order that the next test case is generated in a smaller area of the test case in a local range as much as possible, so that the test case is more uniform in a global angle. In summary, through the above three optimization objective functions, the test cases generated by the test case generation method provided by the present invention will be distributed more "uniformly" in the input space.
The technical scheme of the invention is as follows:
Step 3, evaluating the population according to a second multi-objective function, namely an optimized objective function of the information entropy based on the distanceP；
Step 4, evaluating the population according to a multi-objective function III, namely an optimized objective function based on sample differenceP；
Step 5, selecting, crossing and mutating the population according to the NSGA-II algorithm;
and 6, judging whether the evolution search stopping condition is reached, if not, skipping to the step 2, otherwise, outputting the optimal solution as the next test case and executing the test case.
The specific implementation of the step 1 comprises the following steps:
step 1.1, determining the range and the dimensionality of an input domain according to information given by a tested program;
step 1.2, coding is carried out, one test case is used as a chromosome (individual), genes on the chromosome correspond to numerical values on corresponding dimensions of the test case, the whole test case set is a population, and the size N of the population is 20;
and 1.3, initializing the whole population completely by a random generation method.
The specific implementation of the step 2 comprises the following steps:
step 2.1, judging whether the test case set T is empty, if so, setting the fitness 1 of all individuals to be 0, and ending the function, otherwise, entering step 2.2;
step 2.2, if the set T is not empty, calculating the minimum distance between each individual in the population and all test cases in the set T, wherein the calculation formula of the distance is as follows:
step 2.3, will be beforekAll the maximum minimum distances are saved, and the maximum value is set as the fitness 1 of the individual, whereinkAnd 5 by default, when the number of the test cases in the test case set T is less than 5,kthe number of test cases in the actual test case set T.
The specific implementation of the step 3 comprises the following steps:
step 3.1, judging whether the number of the test case sets T is less than or equal to 1, if so, setting the fitness 2 of all individuals to be 0 and ending the function, otherwise, entering step 3.2;
step 3.2, if the number of the test cases in the test case set is larger than 1 and smaller than a constant valuekThen, thenkIs equal to the number of test cases in the current test case set, whereinkThe initial value is 5;
step 3.3, calculation according to step 2.3Obtained beforekThe maximum minimum distance is calculated, and the information entropy value is set as the fitness 2 of the individual, wherein the information entropyEntropyThe calculation formula is as follows:
d is frontkThe sum of the distances is the sum of the distances,dist i is the ith minimum distance of the individual.
The specific implementation of the step 4 comprises the following steps:
step 4.1, judging whether the number of the test case sets T is less than a constant valueθIf yes, setting the fitness 3 of all individuals to be 0 and ending the function, otherwise, turning to the next step, wherein the default value of the constant theta is 5;
step 4.2, calculating the division number of the input spacepWhereinpThe calculation formula of (2) is as follows:
dis the dimension of the input field;
step 4.3, divide each dimension of the input domain space intopAnd determining each individual and a sub-region where each test case in the set T is located, and calculating a sample difference value of the sub-region as fitness 3, wherein a calculation formula of the sample difference value is as follows:
where D is the size of the sub-region area, S is the size of the entire input region area,tthe number of test cases in the set T is contained in the sub-region, | T | is the number of test cases of the whole test case set.
The specific implementation of the step 5 comprises the following steps:
step 5.1, selecting the population based on NSGA-II algorithm realized by Jenetics framework, wherein the selection algorithm adoptsn-a championship algorithm,nis set to be 2;
step 5.2, performing cross operation on the population based on an NSGA-II algorithm realized by a Jenetics framework, wherein the cross algorithm adopts single-point cross, and the cross probability is set to be 0.6;
step 5.3, performing variation operation on the population based on an NSGA-II algorithm realized by a Jenetics framework, wherein the variation algorithm adopts single-point variation, and the variation probability is 0.1;
and 5.4, evaluating the population based on an NSGA-II algorithm realized by a Jenetics framework to obtain the optimal individual.
The specific implementation of the step 6 comprises the following steps:
step 6.1, setting iteration times for the step 5, outputting an optimal solution as a next test case if the iteration times reach 100, namely the population evolves to 100 generations, and continuing to carry out iterative evolution in the step two if not;
step 6.2, executing the test case, and putting the test case into a test case set T;
and 6.3, judging whether the termination condition for generating the test case is met, wherein the termination condition is that a program is found to be invalid or the size of the test case set T reaches a certain number, if the termination condition is met, ending the method and returning to the test case set T, otherwise, switching to the step 1 to reinitialize the population, and continuing iterative evolution to generate the next test case.
Compared with the prior art, the invention has the beneficial effects that:
1. compared with the eAR single-target optimization function adaptive random test method, the multi-target group intelligent algorithm adopted by the invention can optimize the test cases on a plurality of target components, thereby enhancing the diversity of the test cases and achieving the purpose of uniform distribution of the test cases on a plurality of dimensions. The NSGA-II algorithm adopted by the invention is a popular multi-objective optimization algorithm at present, and can well select the multi-objective optimal solution from a plurality of feasible solutions.
2. The target optimization function adopted by the invention adopts an information entropy optimization function based on distance besides the most classical minimum distance maximization function. The function can ensure that the minimum distance between the test case and the neighbor is uniform as much as possible, so that the boundary effect caused by the maximum target function with the minimum distance is made up, the tendency that the test case is drawn close to the boundary as much as possible is reduced, and the test case has higher probability to be generated in the central area of the input domain.
3. The objective optimization function based on the sample difference is adopted by the invention, so that the test cases are generated in the sub-area with lower density as much as possible, and the density uniformity of each part of the area can be better achieved. The function with the maximized minimum distance and the function based on the information entropy of the distance possibly cause the distribution of the test cases to be not distributed uniformly on the whole, so that the target of the sample difference can better make up the defects of the prior optimized function.
Drawings
FIG. 1 is an algorithm flow chart of an adaptive random test case generation method based on multi-objective group intelligence.
FIG. 2 is a schematic diagram of calculating an objective optimization function based on minimum distance maximization.
FIG. 3 is a schematic diagram of an objective optimization function that calculates entropy of information based on distance.
FIG. 4 is a schematic diagram of calculating an objective optimization function based on sample differences.
FIG. 5 is a plot of the F-ratio line for the results of a two-dimensional block simulation run at 5000 replicates.
FIG. 6 is a plot of the F-ratio line of the results of a three-dimensional block simulation run at 5000 replicates.
Detailed Description
In order to clearly understand the contents of the adaptive random test case generation method based on multi-objective group intelligence, the present invention is further described below with reference to the accompanying drawings and specific embodiments, it should be noted that the embodiments described are intended to facilitate understanding of the present invention, and do not have any limitation on the embodiments.
The overall algorithm flow chart of the method for generating the adaptive random test case based on the multi-target group intelligence is shown in figure 1, and in the first step, a random method is utilized to initialize a population; secondly, evaluating the population according to the three optimization objective functions; thirdly, selecting, crossing and mutating the population by adopting NSGA-II to obtain a new population and cover the original population; the fourth step is to evaluate the generated new population through three objective optimization functions; judging whether the evolution search stopping condition is reached, if the evolution search stopping condition is not reached, switching to the third step to continue the evolution, otherwise, jumping to the next step; the sixth step is to select the optimal individual from the new population after evaluation as the next test case t; the seventh step is to execute the test case t; the eighth step is that the test case T is added into the test case set T; judging whether the termination condition of test case generation is met, if not, switching to the second step to reinitialize the population to continue generating the next test case, otherwise, jumping to the next step; the tenth step is to output the test case set T. The termination condition for the evolutionary search is set to 100 evolutionary iterations, and the termination condition for the test case generation is to find out that the program fails or reaches a certain number of test cases. Three optimization objective function reference sub-processes: the first step is to evaluate the population according to an optimization objective function based on minimum distance maximization; secondly, evaluating the population according to an optimization objective function of the information entropy based on the distance; the third step is to evaluate the population according to an optimized objective function of sample differences.
Referring to fig. 2, an objective optimization function based on minimum distance maximization is calculated as follows:
step 201, traversing each individual in the population, calculating fitness 1 for each individual, and setting the current individual as c;
step 202, traversing the tested case set T, if the number of the tested case set T is 0, directly setting the fitness of all individuals to be 0 and ending the function, otherwise, calculating the minimum distance between the current individual c and all other individuals in the tested case set T;
step 203, the maximum minimum distance is set as fitness 1, and the maximum minimum distance is set as the fitness 1kThe largest minimum distance is stored to facilitate calculation of distance entropy.
Referring to fig. 3, an objective optimization function for calculating information entropy based on distance is as follows:
step 301, traversing each individual in the population, calculating the fitness 2 for each individual, and setting the current individual as c;
step 303, judging whether the number of the test cases in the test case set T is greater than 1, if not, setting the fitness 2 of the individual to be 0 and finishing the function, otherwise, turning to the next step;
step 303, based on the previous data stored in step 203kThe minimum distance calculates the information entropy of the distance according to a formula, and sets the value as fitness 2.
Referring to fig. 4, an objective optimization function based on sample differences is calculated as follows:
step 401, traversing each individual in the population, calculating fitness 1 for each individual, and setting the current individual as c;
step 402, judging whether the number of the test cases of the test case set T is 0, if so, setting the fitness 3 of all individuals to be 0, otherwise, calculating the number of the space division according to a formulapPositioning all test cases in the test case set T to corresponding sub-regions;
step 403, calculating the area of the sub-region where the individual c is located and the number of the test cases containing the test case set T in the sub-region, calculating the sample difference value for the individual c according to a formula, and setting the difference value as fitness 3.
The failure zone model of the simulation experiment was analyzed herein based on a study of the ART algorithm. Finally, two-dimensional and three-dimensional block failure models are constructed, and repeated experiments are carried out on FSCS, eAR and the invention (MOART) under the conditions of four failure rates of 0.01, 0.005, 0.002, 0.001 and the like. Meanwhile, the text adopts an index (F-measure) And a measure (F-ratio) The experimental results of the three algorithms are compared. In addition, F + of every two algorithms will be described hereinmeasureThe values were subjected to rank-sum test for examinationThe significant superiority of the MOART algorithm is tested. The experimental parameters of the genetic algorithm are set as shown in table 1, the crossover operator adopts single-point crossover, the mutation operator adopts single-point mutation, and the selection operator adopts n-championship. The two-dimensional test results are shown in tables 2 and 5, the three-dimensional test result data are shown in tables 3 and 6, the two-dimensional and three-dimensional rank sum test results are shown in tables 4 and 5, and the test results are all 5000 times of repeated experiments.
The experiment result shows that the MOART algorithm has better failure detection capability for a model with higher failure rate in a low-dimensional mode than other two algorithms. With reduced failure rate, the effect of this algorithm is no longer superior to eAR, but still superior to the FSCS algorithm. The MOART algorithm has better failure detection effect in the failure rate range of 0.01-0.001 than the eAR algorithm in the high latitude input domain. The effectiveness of MOART is significantly better than FSCS with decreasing failure rate compared to FSCS.
TABLE 1 Experimental parameters
| Parameter name | Description of the invention | Parameter value |
| N | Size of population | 20 |
| P c | Probability of crossing | 0.6 |
| P t | Probability of variation | 0.1 |
| n | Capacity of championship game | 2 |
| i | Number of iterations of evolution | 100 |
| k | Number of partial maximum minimum distances | 5 |
| θ | Average number of expected test cases in divided sub-regions | 5 |
TABLE 2F-ratio values for each algorithm under the two-dimensional input domain
| FSCS | eAR | MOART | |
| 0.01 | 67.0789 | 67.0457 | 63.5478 |
| 0.005 | 65.6424 | 63.4633 | 61.9725 |
| 0.002 | 64.9998 | 61.4403 | 60.8600 |
| 0.001 | 64.0730 | 59.7913 | 59.1222 |
TABLE 3F-ratio values for various algorithms under three-dimensional input domain
| FSCS | eAR | MOART | |
| 0.01 | 84.8218 | 92.6200 | 82.7448 |
| 0.005 | 80.2536 | 84.7559 | 79.0998 |
| 0.002 | 77.9776 | 77.4597 | 73.4101 |
| 0.001 | 74.8610 | 74.4151 | 70.9618 |
TABLE 4F-measure rank and test results under two-dimensional input Domain
TABLE 5F-measure rank and test results under three-dimensional input Domain
The foregoing is merely for the purpose of illustrating particular embodiments of the invention and is not to be construed as limiting the scope of the invention, as any modifications, alterations and the like may be made without departing from the spirit and scope of the invention.
Claims (7)
1. A method for generating an adaptive random test case based on multi-target group intelligence is characterized by comprising the following steps:
step 1, determining the range of an input domain through information given by a program, and randomly generating a population with the size of NP；
Step 2, evaluating the population according to a multi-objective function I, namely a function based on minimum distance maximizationP；
Step 3, evaluating the population according to a second multi-target function, namely a function of the information entropy based on the distanceP；
Step 4, evaluating the population according to a multi-target function III, namely a function based on sample differenceP；
Step 5, selecting, crossing and mutating the population according to the NSGA-II algorithm;
and 6, judging whether the evolution search stopping condition is reached, if not, skipping to the step 2, otherwise, outputting the optimal solution as the next test case and executing the test case.
2. The method as claimed in claim 1, wherein the specific implementation of step 1 comprises the following steps:
step 1.1, determining the range and the dimensionality of an input domain according to information given by a tested program;
step 1.2, coding is carried out, one test case is used as a chromosome (individual), genes on the chromosome correspond to numerical values on corresponding dimensions of the test case, the whole test case set is a population, and the size N of the population is 20;
and 1.3, initializing the whole population completely by a random generation method.
3. The method as claimed in claim 1, wherein the step 2 is implemented by the following steps:
step 2.1, judging whether the test case set T is empty, if so, setting the fitness 1 of all individuals to be 0, and ending the function, otherwise, entering step 2.2;
step 2.2, if the set T is not empty, calculating the minimum distance between each individual in the population and all test cases in the set T, wherein the calculation formula of the distance is as follows:
step 2.3, will be beforekAll the maximum minimum distances are saved, and the maximum value is set as the fitness 1 of the individual, whereinkAnd 5 by default, when the number of the test cases in the test case set T is less than 5,kthe number of test cases in the actual test case set T.
4. The method as claimed in claim 1, wherein the specific implementation of step 3 comprises the following steps:
step 3.1, judging whether the number of the test case sets T is less than or equal to 1, if so, setting the fitness 2 of all individuals to be 0 and ending the function, otherwise, entering step 3.2;
step 3.2, if the number of the test cases in the test case set is larger than 1 and smaller than a constant valuekThen, thenkIs equal to the number of test cases in the current test case set, whereinkThe initial value is 5;
step 3.3, pre-calculation according to step 2.3kThe maximum minimum distance is calculated, and the information entropy value is set as the fitness 2 of the individual, wherein the information entropyEntropyThe calculation formula is as follows:
d is frontkThe sum of the distances is the sum of the distances,dist i is the ith minimum distance of the individual.
5. The method as claimed in claim 1, wherein the specific implementation of step 4 comprises the following steps:
step 4.1, judging whether the number of the test case sets T is less than a constant valueθIf yes, the fitness 3 of all individuals is set to 0 and the function is ended, otherwise, the next step is carried out, wherein a constant value is obtainedθThe default value is 5;
step 4.2, calculating the division number of the input spacepWhereinpThe calculation formula of (2) is as follows:
dthe dimension of the input domain, and the size of the test case set T is | T |;
step 4.3, divide each dimension of the input domain space intopAnd determining each individual and a sub-region where each test case in the set T is located, and calculating a sample difference value of the sub-region as fitness 3, wherein a calculation formula of the sample difference value is as follows:
where D is the size of the sub-region area, S is the size of the entire input region area,tthe number of test cases in the set T is contained in the sub-region, | T | is the number of test cases of the whole test case set.
6. The method as claimed in claim 1, wherein the specific implementation of the step 5 comprises the following steps:
step 5.1, selecting the population based on NSGA-II algorithm realized by Jenetics framework, wherein the selection algorithm adoptsn-a championship algorithm,nis set to be 2;
step 5.2, performing cross operation on the population based on an NSGA-II algorithm realized by a Jenetics framework, wherein the cross algorithm adopts single-point cross, and the cross probability is set to be 0.6;
step 5.3, performing variation operation on the population based on an NSGA-II algorithm realized by a Jenetics framework, wherein the variation algorithm adopts single-point variation, and the variation probability is 0.1;
and 5.4, evaluating the population based on an NSGA-II algorithm realized by a Jenetics framework to obtain the optimal individual.
7. The method as claimed in claim 1, wherein the specific implementation of the step 6 comprises the following steps:
step 6.1, setting iteration times for the step 5, outputting an optimal solution as a next test case if the iteration times reach 100, namely the population evolves to 100 generations, and continuing to carry out iterative evolution in the step two if not;
step 6.2, executing the test case, and putting the test case into a test case set T;
and 6.3, judging whether the termination condition for generating the test case is met, wherein the termination condition is that a program is found to be invalid or the size of the test case set T reaches a certain number, if the termination condition is met, ending the method and returning to the test case set T, otherwise, switching to the step 1 to reinitialize the population, and continuing iterative evolution to generate the next test case.
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