CN106919504B - Test data evolution generation method based on GA algorithm - Google Patents

Test data evolution generation method based on GA algorithm Download PDF

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CN106919504B
CN106919504B CN201710077936.8A CN201710077936A CN106919504B CN 106919504 B CN106919504 B CN 106919504B CN 201710077936 A CN201710077936 A CN 201710077936A CN 106919504 B CN106919504 B CN 106919504B
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包晓安
郑腾飞
张娜
熊子健
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Abstract

The invention provides a test data evolution generation method based on a GA algorithm. The invention comprises the following steps: introducing the concept of multiple populations, and further considering the influence of individual similarity and population diversity of the populations on test data generation; the influence of the branch distance and the node coverage rate is considered, so that influence factors are added into various improved calculation formulas, some weight factors are added, weight distribution is carried out on the influence factors, and dynamic self-adaptive adjustment of the test case is facilitated. And the algorithm is strengthened in the aspect of the operational capability through a dynamic self-adaptive adjustment strategy of the cross rate and the variation rate. The contribution degree of the individual is calculated, and then the traditional adaptive value function is adjusted, so that excellent individual can be saved in later evolution, and the test data generation efficiency is improved.

Description

Test data evolution generation method based on GA algorithm
Technical Field
The invention belongs to the field of software testing, and particularly relates to a test data evolution generation method based on a GA algorithm.
Background
Software testing is a crucial link for ensuring software quality in software system development, and software development cost has a great proportion to be used in testing. If the testing process can be automated, the cost of software development can be reduced to a great extent, and the testing efficiency is improved. The generation work of the test case comprises the steps of determining test requirements, determining input data, operating the tested program and analyzing corresponding output data. The design of the automatic test case generation technology is an important problem of software automatic test, and the solution of the problem is very important for ensuring the software quality and improving the reliability of the software quality.
The genetic algorithm is used as a heuristic search algorithm and has the advantages of simplicity, practicability, strong universality, high robustness, strong global search capability and the like. The genetic algorithm is an effective method for solving the problem of automatic generation of test data, and related research results at home and abroad are more in recent years. However, some inherent defects of the genetic algorithm, such as premature stagnation, easy falling into local optimality, low efficiency of late search, and the like, affect the efficiency of test generation. In addition, the existing adaptive value function design method does not effectively utilize the comprehensive information reflected by the evolution population, so that the generated test data is not well protected in the evolution process.
Therefore, aiming at the defects of the genetic algorithm, the genetic algorithm is improved, the coverage rate, the population diversity and the branch distance of the test nodes are used as main design parameters of the test case adaptive value function, the excellence degree of the test case is greatly improved, and the test case obtained by the method is an optimal set for the generation execution efficiency and the coverage rate of the test data. The method has the advantages that the running capability in practice is enhanced, and the method has good advantages in the aspects of coverage rate, test case scale and search time.
Disclosure of Invention
The invention aims to introduce an individual contribution value, a crossing rate and a variation rate dynamic self-adaptive adjustment strategy aiming at the defects of a genetic algorithm and the defects of the existing method in designing an adaptive value function on the basis of a classical genetic algorithm, so that excellent individuals can be better stored, and the calculation capability of the algorithm is enhanced.
The technical scheme adopted by the invention for solving the technical problems is as follows: introducing the concept of multiple populations, and further considering the influence of individual similarity and population diversity of the populations on test data generation; the influence of the branch distance and the node coverage rate is considered, so that influence factors are added into various improved calculation formulas, some weight factors are added, weight distribution is carried out on the influence factors, and dynamic self-adaptive adjustment of the test case is facilitated. And the algorithm is strengthened in the aspect of the operational capability through a dynamic self-adaptive adjustment strategy of the cross rate and the variation rate. The contribution degree of the individual is calculated, and then the traditional adaptive value function is adjusted, so that excellent individual can be saved in later evolution, and the test data generation efficiency is improved.
In order to achieve the aim, the invention provides a test data generation method based on a GA algorithm. The method comprises the following specific steps:
1) initializing a sub-population, coding in a bit string form, and establishing a one-to-one mapping relation between individual chromosomes of the population and a binary string;
2) calculating individual distances of the population; calculating the node coverage rate and the branch distance; then constructing an individual adaptive value function; calculating the adaptive value of each individual in the sub-population, and sorting the adaptive values in descending order;
introducing a node contribution degree, adjusting an adaptive value function according to the weight of the contribution degree, calculating an adjusted adaptive value function value, and taking the adjusted adaptive value function value as a threshold value;
3) selecting individuals with the adaptive values larger than the threshold value in each sub-population to form a main population, sorting the individuals in the main population in a descending order according to the adaptive values, selecting the individuals 1/2 before sorting, and replacing the individuals with the adaptive values smaller than the threshold value in each sub-population;
4) sequentially carrying out selection, crossing and mutation operations on each sub-population processed in the step 3) to generate a sub-population;
5) judging whether a set termination condition is met, if so, stopping the algorithm and outputting test data; otherwise, jumping to step 2).
Preferably, the step 2) is specifically:
step a: the individual distance formula for calculating the population is as follows:
Figure GDA0002516617250000021
wherein d (x, y) represents the Manhattan distance between two individuals, xiAnd xjRepresents any two different individuals in the kth population;
step b: the calculation formula of the branch distance is as follows:
Figure GDA0002516617250000031
in the formula, OdistanceRepresenting the branch distance, S is the number of paths covering the target path, θ is a constant greater than zero;
step c: the calculation formula of the node coverage rate is as follows:
Figure GDA0002516617250000032
in the formula, NcrRepresenting the node coverage rate, wherein tau is the node number covering the target path node, and omega is the node number of the target path;
step d, the individual fitness value function is as follows:
fit(xi，t)＝αOdistance(xi,t)+βNcr(xi,t)+γd(xi,xj,t)
in the formula, fit (x)iT) represents an individual fitness value function, Odistance(xi,t)、Ncr(xiT) and d (x)i,xjT) are the individuals x in the kth population of the tth generation respectivelyiα + β + γ being 1, α being a coverage factor, β being a branch predicate factor, γ being a population diversity factor;
step e: the calculation formula of the population diversity is as follows:
Vk＝ηDk+(1-η)fitk
in the formula, VkIndicates the diversity of the population, DkIs the Manhattan distance of the population
Figure GDA0002516617250000033
fitkIs the adaptive value of an individual, η is a constant which is not zero, k represents the current population, k is more than or equal to 1 and less than or equal to m, and m is the total number of the sub-populations;
and f, adjusting the original adaptive value function:
Figure GDA0002516617250000034
where Fit represents the adjusted fitness function, Q (x)iT) is the individual xiThe degree of contribution of (c).
Preferably, the step 3) is specifically:
step a: the selection operation adopts a method of combining an improved truncation selection method and an elite individual retention strategy to select the mother individual, and the selection probability is as follows:
Figure GDA0002516617250000041
in the formula, Pk(xi) Is an individual x in the kth populationiProbability of being selected as parent, SKIs the number of individuals in the population that are greater than a threshold;
step c: the cross operation achieves the dynamic self-adaptive change of the cross probability by introducing the similarity of population individuals.
Population individual xiAnd xjThe similarity between them is:
Figure GDA0002516617250000042
in the formula, xigThe g-th symbol in the binary string representing the i-th input variable, l being the length of the binary string;
c, the mutation operation adopts dynamic self-adaption to change the mutation rate PmTo enhance population diversity. The mutation rate:
Figure GDA0002516617250000043
in the formula (f)maxIs the maximum fitness value in the current population,
Figure GDA0002516617250000044
is the average fitness value, V, in the current populationkIs the diversity value of the current population, and p is the actually set small variation rate.
The genetic operation is beneficial to keeping better individuals in the population, ensures the diversity of the population, enhances the searching capability and prevents the premature stagnation of the individuals from appearing.
Drawings
Fig. 1 is a flow chart of the algorithm.
Fig. 2 is a population preference model.
FIG. 3 is a control flow diagram of a triangle classifier segment.
Fig. 4 is a flow chart of the construction of the fitness function.
Detailed Description
The invention is described in detail below with reference to the drawings and specific embodiments, but the invention is not limited thereto.
FIG. 1 is a flowchart of the test data evolution generation method of GA algorithm implemented in the present invention. The method comprises the following specific steps:
1) converting the feasible solution of the problem from the solution space to a search space which can be processed by a genetic algorithm, initializing a sub-population, assigning values to parameters of the algorithm, and adopting a bit string form for coding to establish a one-to-one mapping relation between individual chromosomes of the population and a binary string; this binary string can be represented as:
Figure GDA0002516617250000051
2) inserting a tested program, inputting a test case into the program to execute individual distance calculation of a population; calculating the node coverage rate and the branch distance; then constructing an individual adaptive value function; calculating the adaptive value of each individual in the sub-population, and sorting the adaptive values in descending order; introducing a node contribution degree, adjusting an adaptive value function according to the weight of the contribution degree, calculating an adjusted adaptive value function value, and taking the adjusted adaptive value function value as a threshold value;
3) selecting individuals with the adaptive values larger than the threshold value in each sub-population to form a main population, sorting the individuals in the main population in a descending order according to the adaptive values, selecting the individuals 1/2 before sorting, and replacing the individuals with the adaptive values smaller than the threshold value in each sub-population;
4) sequentially carrying out selection, crossing and mutation operations on each sub-population processed in the step 3) to generate a sub-population;
5) judging whether a set termination condition is met, if so, stopping the algorithm, decoding the stored individuals and outputting test data; otherwise, jumping to step 2). The termination condition here is whether the iteration satisfies the set number N, which is 100 times in this embodiment.
Fig. 2 is a population preference model. FIG. 3 is a control flow diagram of a triangle classifier segment. In a control flow graph, each branch can be represented by a conditional expression called a branch predicate, and the function of the conditional expression is to describe the condition of a program traversing the statement under the branch, such as judging the branch predicate a > b in the statement if (a > b). When the branch predicate is true, the branch function is taken to be zero; when the branch predicate is false, the branch function of the result is calculated, and the value of the branch function is positive at this time. And inserting branch functions before each branch statement passed by the logic path of the program unit by using branch function instrumentation in the actual execution process. When the test case executes the tested program unit, the value of the score function can be obtained.
(1) And calculating an adaptive value.
Fig. 4 is a flow chart of the construction of the fitness function. Firstly, calculating individual distances of a population; then, calculating the node coverage rate and the branch distance; then, constructing an individual adaptive value function; and finally, introducing the contribution degree of the nodes, adjusting the original adaptive value function by combining the population diversity, and taking the original adaptive value function as a threshold value. The method comprises the following specific steps:
step a: the individual distance formula for calculating the population is as follows:
Figure GDA0002516617250000061
wherein d (x, y) represents the Manhattan distance between two individuals, xiAnd xjRepresenting any two different individuals in the kth population.
Step b: the branch distance (offtake) is calculated by the formula:
Figure GDA0002516617250000062
in the formula, OdistanceDenotes the branch distance, S is the number of paths covering the target path, and θ is a constant greater than zero.
Step c: the node coverage rate (node coverage rate) is calculated by the following formula:
Figure GDA0002516617250000063
in the formula, NcrAnd expressing the node coverage rate, wherein tau is the number of nodes covering the nodes of the target path, and omega is the number of nodes of the target path.
Step d, the individual fitness value function is as follows:
fit(xi，t)＝αOdistance(xi,t)+βNcr(xi,t)+γd(xi,xj,t)
in the formula, fit (x)iT) represents an individual fitness value function, Odistance(xi,t)、Ncr(xiT) and d (x)i,xjT) are the individuals x in the kth population of the tth generation respectivelyiThe branch distance, node coverage rate, and individual distance of α + β + γ equals 1.
Step e: the calculation formula of the population diversity is as follows:
Vk＝ηDk+(1-η)fitk
in the formula, VkIndicates the diversity of the population, DkIs the Manhattan distance of the population
Figure GDA0002516617250000064
fitkIs the fitness value of the individual, η is a non-zero constant, k represents the current population (1. ltoreq. k. ltoreq. m).
And f, adjusting the original adaptive value function:
Figure GDA0002516617250000065
where Fit represents the adjusted fitness function (as a threshold), Q (x)iT) is the individual xiThe degree of contribution of (c).
(2) And (4) preferentially replacing population individuals.
And selecting individuals with the adaptive values larger than the threshold value in each sub-population to form a main population, sorting the individuals in the main population in a descending order according to the adaptive values, selecting the first 1/2 individuals, and replacing the individuals with the adaptive values smaller than the threshold value in each sub-population.
(3) And (4) carrying out genetic manipulation.
Selecting mother individuals by combining a truncation method and an elite individual retention strategy, replacing and recombining partial structures of the two mother individuals to generate new individuals, and changing genes in the split bodies with a certain probability by using a mutation operator. The specific steps of genetic manipulation are as follows:
step a: the selection operation adopts a method of combining an improved truncation selection method and an elite individual retention strategy to select the mother individual. The selection probability is:
Figure GDA0002516617250000071
in the formula, Pk(xi) Is an individual x in the kth populationiProbability of being selected as parent, SKIs the number of individuals in the population that are greater than the threshold.
Step c: the cross operation achieves the dynamic self-adaptive change of the cross probability by introducing the similarity of population individuals.
Population individual xiAnd xjThe similarity between them is:
Figure GDA0002516617250000072
in the formula, xigThe g-th symbol in the binary string representing the i-th input variable, l is the length of the binary string.
C, the mutation operation adopts dynamic self-adaption to change the mutation rate PmTo enhance population diversity. The mutation rate:
Figure GDA0002516617250000073
in the formula (f)maxIs the maximum fitness value in the current population,
Figure GDA0002516617250000074
is the average fitness value, V, in the current populationkIs the diversity value of the current population, and p is the actually set small variation rate.
In conclusion, compared with the basic genetic algorithm, the improved algorithm provided by the invention overcomes the defects of early-maturing stagnation, easy falling into local optimum, low later-stage search efficiency and the like inherent in the genetic algorithm, greatly improves the running capability in practice, and has good advantages in the aspects of coverage rate, test case scale and search time, and the efficiency and protection of generating test data.

Claims (3)

1. A test data evolution generation method based on GA algorithm is characterized by comprising the following steps:
1) initializing a sub-population, assigning algorithm parameters, coding in a bit string form, and establishing a one-to-one mapping relation between individual chromosomes of the population and a binary string;
2) inserting a tested program, inputting a test case into the program to execute individual distance calculation of a population; calculating the node coverage rate and the branch distance; then constructing an individual adaptive value function; calculating the adaptive value of each individual in the sub-population, and sorting the adaptive values in descending order; introducing a node contribution degree, adjusting an adaptive value function according to the weight of the contribution degree, calculating an adjusted adaptive value function value, and taking the adjusted adaptive value function value as a threshold value;
3) selecting individuals with the adaptive values larger than the threshold value in each sub-population to form a main population, sorting the individuals in the main population in a descending order according to the adaptive values, selecting the individuals 1/2 before sorting, and replacing the individuals with the adaptive values smaller than the threshold value in each sub-population;
4) sequentially carrying out selection, crossing and mutation operations on each sub-population processed in the step 3) to generate a sub-population;
5) judging whether a set termination condition is met, if so, stopping iteration and outputting test data; otherwise, jumping to step 2).
2. A GA algorithm-based test data evolution generation method according to claim 1, wherein the step 2) specifically comprises:
step a: the individual distance formula for calculating the population is as follows:
Figure FDA0002516617240000011
wherein d (x, y) represents the Manhattan distance between two individuals, xiAnd xjRepresents any two different individuals in the kth population;
step b: the calculation formula of the branch distance is as follows:
Figure FDA0002516617240000012
in the formula, OdistanceRepresenting the branch distance, S is the number of paths covering the target path, θ is a constant greater than zero;
step c: the calculation formula of the node coverage rate is as follows:
Figure FDA0002516617240000013
in the formula, NcrRepresenting the node coverage rate, wherein tau is the node number covering the target path node, and omega is the node number of the target path;
step d, the individual fitness value function is as follows:
fit(xi，t)＝αOdistance(xi,t)+βNcr(xi,t)+γd(xi,xj,t)
in the formula, fit (x)iT) represents an individual fitness value function, Odistance(xi,t)、Ncr(xiT) and d (x)i,xjT) are the individuals x in the kth population of the tth generation respectivelyiα + β + γ being 1, α being a coverage factor, β being a branch predicate factor, γ being a population diversity factor;
step e: the calculation formula of the population diversity is as follows:
Vk＝ηDk+(1-η)fitk
in the formula, VkIndicates the diversity of the population, DkIs the Manhattan distance of the population
Figure FDA0002516617240000021
fitkIs the adaptive value of an individual, η is a constant which is not zero, k represents the current population, k is more than or equal to 1 and less than or equal to m, and m is the total number of the sub-populations;
and f, adjusting the original adaptive value function:
Figure FDA0002516617240000022
where Fit represents the adjusted fitness function, Q (x)iT) is the individual xiThe degree of contribution of (c).
3. A GA algorithm-based test data evolution generation method according to claim 1, wherein the step 3) specifically comprises:
step a: the selection operation adopts a method of combining an improved truncation selection method and an elite individual retention strategy to select the mother individual, and the selection probability is as follows:
Figure FDA0002516617240000023
in the formula, Pk(xi) Is an individual x in the kth populationiProbability of being selected as parent, SKIs the number of individuals in the population that are greater than a threshold;
step c: the cross operation achieves the dynamic self-adaptive change of cross probability by introducing the similarity of population individuals;
population individual xiAnd xjThe similarity between them is:
Figure FDA0002516617240000031
in the formula, xigThe g-th symbol in the binary string representing the i-th input variable, l being the length of the binary string;
c, the mutation operation adopts dynamic self-adaption to change the mutation rate PmTo enhance population diversity; the mutation rate:
Figure FDA0002516617240000032
in the formula (f)maxIs the maximum fitness value in the current population,
Figure FDA0002516617240000033
is the average fitness value, V, in the current populationkIs the diversity value of the current population, and p is the actually set small variation rate.
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