WO2022134581A1 - Test case sorting method and related device - Google Patents

Abstract

The present application relates to the fields of artificial intelligence and software testing, and provides a test case sorting method and a related device. The method comprises: initializing a test case set to obtain a primary-generation test case sequence population; encoding the primary-generation test case sequence population; evaluating a test case sequence in the primary-generation test case sequence population by using a selection function, obtaining a child-generation test case sequence population by using a selection, crossover, and/or serial number variation method, and merging the child-generation test case sequence population and the primary-generation test case sequence population to obtain a merged population; and performing non-dominated sorting on the population, obtaining a next-generation population by using a congestion degree comparison operator, obtaining an optimal solution set by circulating the foregoing method, and outputting the optimal solution set. By performing rapid non-dominated sorting on the test cases, the present application improves the fault detection and regression testing efficiency of software, thereby reducing the test cost of the software.

Description

Test case sequencing method and related equipment

This application claims the priority of the Chinese patent application with the application number 202011552451.8 and the invention titled "Test Case Ranking Method and Electronic Device" filed with the China Patent Office on December 24, 2020, the entire contents of which are incorporated in this application by reference .

technical field

The present application relates to the field of software testing, in particular to a test case sorting method and related equipment.

Background technique

With the development of artificial intelligence, the scope of software requirements continues to expand. In the existing technology, due to the continuous expansion of the scope of software requirements, developers continue to iteratively modify the software, resulting in a larger and more complex set of test cases. The main problem is how to efficiently use the existing test cases to test the software, and the test case prioritization is one of the ways to solve the high-efficiency software testing. Test case prioritization is to set the limited level of test cases according to the established test objectives without reducing the number of test cases, so that the test cases with higher priority are limitedly executed, and the purpose is to detect failures as early as possible to speed up failures positioning, thereby reducing the cost of regression testing. The inventor realizes that most of the existing test case sorting is based on single-objective optimization, and the current software program is increasingly complex, and the single-objective test case prioritization technology cannot meet the needs of regression testing.

SUMMARY OF THE INVENTION

In view of the above content, it is necessary to propose a test case sorting method and related equipment to realize quick sorting of test cases.

A first aspect of the present application provides a method for sorting test cases, and the method for sorting test cases includes:

Obtaining a test case set, arranging the test cases in the test case set in random order to obtain a first-scale test case sequence, and using the first-scale test case sequence as a population of first-generation test case sequences;

performing binary coding on each test case sequence in the primary test case sequence population;

Use a selection function to evaluate the binary-coded primary test case sequence population to obtain the evaluation value of the test case sequence, and perform random selection without playback on the primary test case sequence population according to the evaluation value of each test case sequence, and use The single-point crossover operator and/or the sequence number mutation operator calculates the test case sequence obtained by random selection without playback to obtain the first-generation progeny test case sequence population, and combines the first-generation progeny test case sequence population with the The first-generation test case sequence population obtains the second-scale first-generation test case sequence merged population;

Perform non-dominated sorting on the combined population of the first-generation test case sequences, calculate the crowding degree of the combined population of the first-generation test case sequences, and use a crowding degree comparison operator to test the first generation according to the crowding degree The use case sequence is combined with the population for calculation to obtain the first-generation test case sequence population of the first scale, and the number of iterations g=1;

A preset operation is performed on the first-generation test case sequence population until g is equal to a preset threshold, and the g-th generation test case sequence population is used as the optimal solution set, and the optimal solution set is output.

A second aspect of the present application provides an electronic device, the electronic device includes a processor and a memory, the processor is configured to execute computer-readable instructions stored in the memory to implement the following steps:

Obtaining a test case set, arranging the test cases in the test case set in random order to obtain a first-scale test case sequence, and using the first-scale test case sequence as a population of first-generation test case sequences;

performing binary coding on each test case sequence in the primary test case sequence population;

Use a selection function to evaluate the binary-coded primary test case sequence population to obtain the evaluation value of the test case sequence, and perform random selection without playback on the primary test case sequence population according to the evaluation value of each test case sequence, and use The single-point crossover operator and/or the sequence number mutation operator calculates the test case sequence obtained by random selection without playback to obtain the first-generation progeny test case sequence population, and combines the first-generation progeny test case sequence population with the The first-generation test case sequence population obtains the second-scale first-generation test case sequence merged population;

Perform non-dominated sorting on the combined population of the first-generation test case sequences, calculate the crowding degree of the combined population of the first-generation test case sequences, and use a crowding degree comparison operator to test the first generation according to the crowding degree The use case sequence is combined with the population for calculation to obtain the first-generation test case sequence population of the first scale, and the number of iterations g=1;

A preset operation is performed on the first-generation test case sequence population until g is equal to a preset threshold, and the g-th generation test case sequence population is used as the optimal solution set, and the optimal solution set is output.

A third aspect of the present application provides a computer-readable storage medium on which at least one computer-readable instruction is stored, and the at least one computer-readable instruction is executed by a processor to implement the following steps:

Obtaining a test case set, arranging the test cases in the test case set in random order to obtain a first-scale test case sequence, and using the first-scale test case sequence as a population of first-generation test case sequences;

performing binary coding on each test case sequence in the primary test case sequence population;

Use a selection function to evaluate the binary-coded primary test case sequence population to obtain the evaluation value of the test case sequence, and perform random selection without playback on the primary test case sequence population according to the evaluation value of each test case sequence, and use The single-point crossover operator and/or the sequence number mutation operator calculates the test case sequence obtained by random selection without playback to obtain the first-generation progeny test case sequence population, and combines the first-generation progeny test case sequence population with the The first-generation test case sequence population obtains the second-scale first-generation test case sequence merged population;

Perform non-dominated sorting on the combined population of the first-generation test case sequences, calculate the crowding degree of the combined population of the first-generation test case sequences, and use a crowding degree comparison operator to test the first generation according to the crowding degree The use case sequence is combined with the population for calculation to obtain the first-generation test case sequence population of the first scale, and the number of iterations g=1;

A preset operation is performed on the first-generation test case sequence population until g is equal to a preset threshold, and the g-th generation test case sequence population is used as the optimal solution set, and the optimal solution set is output.

A fourth aspect of the present application provides an apparatus for sorting test cases, the apparatus for sorting test cases includes:

a population initialization module, used for acquiring a test case set, arranging the test cases in the test case set in random order to obtain a first-scale test case sequence, and using the first-scale test case sequence as the first-generation test case sequence population;

an encoding module, configured to perform binary encoding on each test case sequence in the primary test case sequence population;

The merging module is configured to use a selection function to evaluate the binary-coded population of the first-generation test case sequences to obtain evaluation values of the test case sequences, and perform no playback on the population of the first-generation test case sequences according to the evaluation value of each test case sequence Randomly select, and use the single-point crossover operator and/or the sequence number mutation operator to calculate the test case sequence obtained by random selection without playback to obtain the first-generation progeny test case sequence population, and combine the first-generation progeny test cases. The sequence population and the first-generation test case sequence population obtain a second-scale first-generation test case sequence merged population;

The sorting module is configured to perform non-dominated sorting on the combined population of the first-generation test case sequences, calculate the crowding degree of the combined population of the first-generation test case sequences, and use a crowding degree comparison operator to perform a sorting of all the combined populations according to the crowding degree. The first-generation test case sequence is combined with the population for calculation to obtain the first-scale first-generation test case sequence population, and the number of iterations g=1;

The output module is configured to perform a preset operation on the first-generation test case sequence population until g is equal to a preset threshold value, take the g-th generation test case sequence population as the optimal solution set, and output the optimal solution set.

It can be seen from the above technical solutions that in this application, the initial test case set is initialized to obtain the first generation test case sequence population, the first generation test case sequence population is encoded, the selection function is used to evaluate the test case sequence in the first generation test case sequence population, and the selection, Crossover and/or serial number mutation method to obtain the descendant test case sequence population, merge the descendant test case population and the original test case population to obtain the merged population, use to sort the population, and use the crowding degree comparison operator to obtain the next generation population, through the loop The method of selecting, crossing and/or mutating the test case sequence according to the number of iterations and using the non-dominated sorting genetic algorithm based on Pareto optimality and the crowding degree comparison operator to obtain the optimal solution set and output the optimal test case Sequence, through the rapid non-dominant sorting of test cases, improves the efficiency of software fault detection and regression testing, thereby reducing software testing costs.

Description of drawings

FIG. 1 is a flowchart of a method for sorting test cases in an embodiment of the present application.

FIG. 2 is a structural diagram of an apparatus for sorting test cases in an embodiment of the present application.

FIG. 3 is a schematic diagram of an electronic device in an embodiment of the present application.

Fig. 4 is a step of obtaining a first-scale descendant test case sequence population from the primary test case sequence population by using a random selection operator without playback, a single-point crossover operator and/or a sequence number mutation operator according to the evaluation value in the present application Schematic.

FIG. 5 is a schematic diagram of steps of obtaining an initial resource pool by using a random selection operator without playback according to the evaluation value in this application.

Detailed ways

In order to more clearly understand the above objects, features and advantages of the present application, the present application will be described in detail below with reference to the accompanying drawings and specific embodiments. It should be noted that the embodiments of the present application and the features in the embodiments may be combined with each other in the case of no conflict.

In the following description, many specific details are set forth to facilitate a full understanding of the present application, and the described embodiments are only a part of the embodiments of the present application, but not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work fall within the protection scope of the present application.

Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the technical field to which this application belongs. The terms used herein in the specification of the application are for the purpose of describing specific embodiments only, and are not intended to limit the application.

Preferably, the test case sorting method of the present application is applied in one or more electronic devices. The electronic device is a device that can automatically perform numerical calculation and/or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (ASICs) , programmable gate array (Field-Programmable Gate Array, FPGA), digital processor (Digital Signal Processor, DSP), embedded equipment, etc.

The electronic device may be a computing device such as a desktop computer, a notebook computer, a tablet computer, and a cloud server. The device can perform human-computer interaction with the user through a keyboard, a mouse, a remote control, a touch pad, or a voice-activated device.

Example 1

FIG. 1 is a flowchart of a method for sorting test cases in an embodiment of the present application. According to different requirements, the order of the steps in the flowchart can be changed, and some steps can be omitted.

The test case sorting method can acquire and process related data based on artificial intelligence technology. Among them, Artificial Intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. .

The basic technologies of artificial intelligence generally include technologies such as sensors, special artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation/interaction systems, and mechatronics. Artificial intelligence software technology mainly includes computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning/deep learning.

Referring to Figure 1, the test case sorting method specifically includes the following steps:

Step S11 , acquiring a test case set, arranging the test cases in the test case set in random order to obtain a first-scale test case sequence, and using the first-scale test case sequence as the first-generation test case sequence population.

In at least one embodiment of the present application, the size of the primary test case sequence population is determined according to the number of test cases in the test case set.

Specifically, the test case set is composed of test cases, the test case sequence is obtained by arranging the test cases in the test case set in random order, and the test case sequence population is composed of test case sequences.

Step S12, binary coding is performed on each test case sequence in the primary test case sequence population.

In at least one embodiment of the present application, the binary encoding of each test case sequence in the primary test case sequence population includes:

The test case sequence in the primary test case sequence population is encoded according to the locus of the test case sequence in a binary coding manner, and the locus of the test case sequence is the serial number of the corresponding test case.

For example, when the serial number of the test case in a test case sequence is [0000, 0001, 0101, 1001, 1011], the code of the test case sequence may be "00000001010110011011".

Step S13, using a selection function to evaluate the binary-coded first-generation test case sequence population to obtain an evaluation value of the test case sequence, and randomly select the first-generation test case sequence population without playback according to the evaluation value of each test case sequence. , and use the single-point crossover operator and/or the serial number mutation operator to calculate the test case sequence randomly selected without playback to obtain the first-generation progeny test case sequence population, and combine the first-generation progeny test case sequence population A combined population of first-generation test-case sequences of a second scale is obtained with the primary-generation test-case sequence population.

In at least one embodiment of the present application, the evaluation value of the test case sequence obtained by evaluating the binary-coded first-generation test case sequence population by using a selection function includes:

According to the formula

Evaluate the population of the first-generation test case sequence of the first scale to obtain the evaluation value of the test case sequence

in,

represents the similarity of the test case sequence,

Represents the dissimilarity of the test case sequence;

The calculation method is

Among them, S(x _i ,x _j ) represents the similarity between x _i and x _j , and the calculation method is:

The calculation method is:

Among them, D(x _i , x _j ) represents the dissimilarity between x _i and x _j , and the calculation method is:

In at least one embodiment of the present application, α and β take a value of 0.5, and in other embodiments of the present application, α and β can take other values.

In at least one embodiment of the present application, as shown in FIG. 4 , the first-generation test case sequence population is randomly selected without playback according to the evaluation value of each test case sequence, and a single-point crossover operator and/or Or the sequence number mutation operator calculates the test case sequence obtained by random selection without playback to obtain the first-generation descendant test case sequence population including:

The creating step is to create the first-generation progeny test case sequence population;

In the random selection step, according to the evaluation value of each test case sequence, the initial resource pool is obtained by randomly selecting the population of the first-generation test case sequence without playback;

The crossover mutation step is to use the single-point crossover operator and/or the sequence number mutation operator to crossover and mutate the test case sequence in the initial resource pool, and add the crossover and mutated test case sequence to the test case sequence. Describe the descendant test case sequence population;

In the population generation step, the random selection step and the crossover mutation step are repeated until the population size of the progeny test case sequence is the first size.

In at least one embodiment of the present application, as shown in FIG. 5 , according to the initial test case sequence population, according to the evaluation value, the initial resource pool obtained by using a random selection operator without playback includes:

In the calculation step of the expected number of survival, the expected number of survival M of each test case sequence in the first generation test case sequence population in the next generation is calculated according to the formula M=f _i /∑f _i /n, where f _i is the test case sequence i The objective function value of , n is the size of the population of the first-generation test case sequence;

In the random selection step, according to the evaluation value, the test case sequence in the initial test case sequence population is randomly selected to enter the initial resource pool, if the test case sequence in the initial resource pool is selected and performed with the single-point crossover operator. operation, then the expected survival number M in the next generation of the test case sequence in the initial resource pool is minus 0.5, if the test case sequence in the initial resource pool is not selected and not related to the single point If the crossover operator is performed, the expected number of survival of the test case sequence in the initial resource pool in the next generation is subtracted by 1;

The random selection step is repeated until the expected survival number of the test case sequence in the initial resource pool is less than 0.

In at least one embodiment of the present application, when the survival expectation of the test case sequence in the initial resource pool is less than 0, the test case sequence in the initial resource pool is no longer selected.

In at least one embodiment of the present application, the objective function value is calculated by an objective function, and the objective function includes an average fault detection rate APFD (Average Percentage of Faults Detected), a statement coverage rate APSC (Average Percentage of Statement Coverage) , Effective execution time EET (Effective Execution Time).

In at least one embodiment of the present application, the average failure detection rate is based on the formula

Calculated, where APFD is the average failure detection rate, n is the number of test cases in the test case set T, m ₁ is the total number of software defects under test, and TF _i represents the first detection in the sequence of test cases T' after sorting The order in which the test cases for defect i are placed in this execution order.

In this embodiment, the average failure detection rate is used to calculate the average cumulative proportion of defects detected during the execution of the test case.

In at least one embodiment of the present application, the statement coverage is based on the formula

Calculated, where APSC is the statement coverage rate, n is the number of test cases in the test case set T, m ₂ is the total number of software statements under test, TS _i represents the first coverage of the i-th test case sequence T' after sorting The order in which the test cases of the line of code are placed in this execution order.

In this embodiment, the statement coverage rate is used to calculate the ratio of the number of executable statements to the number of all executable statements used when the test case is executed.

In at least one embodiment of the present application, the effective execution time is based on the formula

Calculated, wherein, EET is the effective execution time, and ET _i represents the time consumed when executing the i-th test case.

In this embodiment, the effective execution time is used to calculate the total execution time when the fault detection rate and the statement coverage rate of the test case reach the maximum.

In at least one embodiment of the present application, using a single-point crossover operator to cross the test cases in the initial resource pool includes:

randomly select at least one pair of parent test case sequences in the initial resource pool;

for each pair of parent test case sequences of the at least one pair of parent test case sequences, perform single-point crossover;

The single point intersection includes:

For a pair of parent test case sequences, No. 1 parent test case sequence and No. 2 parent test case sequence, randomly select the intersection position k, k∈[0, L], L is the coding length of the test case sequence, and the No. 1 parent test case The first k genes of the test case sequence are directly used as the first k genes of the sub-test case sequence No. 1, and the genes in the parent test case sequence No. 2 that are the same as the first k genes of the parent test case sequence No. 1 are removed to obtain a length of is the new gene fragment of L-k, and takes it as the last L-k genes of the sub-test case sequence No. 1, and uses the same method to generate the sub-test case sequence No. 2.

For example, when the No. 1 parent test case sequence is [A, B, C, D, E, F, G], the No. 2 parent test case sequence is [G, D, A, C, E, B, F], the coding length of the test case sequence is 7, the cross position 3 is randomly selected, and the first 3 genes of the No. 1 parent test case sequence are directly used as the first 3 genes of the No. 1 child test case sequence genes, namely [A, B, C], remove the same genes in the No. 2 parent test case sequence as the first three genes in the No. 1 parent test case sequence to obtain a new gene with a length of 4 Fragment [G, D, E, F], as the last 4 genes of the sub-test case sequence No. 1, the sub-test case sequence No. 1 is [A, B, C, G, D, E , F], using the same method to obtain the No. 2 sub-test case sequence [G, D, A, B, C, E, F].

In at least one embodiment of the present application, using the sequence number mutation operator to mutate the test case sequence in the initial resource pool includes:

Randomly select at least one test case sequence in the initial resource pool, for each test case sequence in the at least one test case sequence, randomly select two loci on the test case sequence, and combine the two loci of the test case sequence. value is exchanged.

For example, a test case sequence [A, B, C, D, E, F, G] is randomly selected, and two loci C and G on the test case sequence are randomly selected. Swap the two loci to get [A, B, G, D, E, F, C].

Step S14: Perform non-dominant sorting on the combined population of the first-generation test case sequences, calculate the crowding degree of the combined population of the first-generation test case sequences, and use a crowding degree comparison operator to rank the first-generation test case sequence combined population according to the crowding degree. The first-generation test case sequence population is calculated by merging the population of the first-generation test case sequence, and the number of iterations g=1.

In at least one embodiment of the present application, the non-dominated sorting of the first-generation test case sequence merged population includes:

The calculation step is to calculate the fault detection rate, statement coverage rate and effective execution time of all test case sequences in the population, and use the fault detection rate, the statement coverage rate and the effective execution time as a sorting objective function, Get three arrays of objective function values;

A sorting step, creating an external non-dominated solution set, with the goal of maximizing the fault detection rate, maximizing the statement coverage, and minimizing the effective execution time, according to the three arrays consisting of objective function values, Obtain paired non-dominated solutions, add the paired non-dominated solutions to the external non-dominated solution set, compare the test case sequences in the external non-dominated solution set pairwise, and remove the solutions that are not non-dominated solutions, so that The test case sequences in the external non-dominated solution set are mutually non-dominated solutions, wherein the non-dominated solutions are test case sequences of non-dominated relationships;

The solution set generation step is to perform the calculation step and the sorting step on all test case sequences in the merged population of the first-generation test case sequences except the test case sequences in the external non-dominated solution set, to obtain a non-dominated ranking The subsequent external non-dominated solution set series, the failure detection rate of any test case in the external non-dominated solution set ranked first in the non-dominated sorted external non-dominated solution set series is greater than that of the external non-dominated solution set after the ranking. The failure detection rate of all test cases in the set, and the statement coverage rate of any test case in the external non-dominated solution set ranked first in the external non-dominated solution set series after the non-dominated sorting is greater than that of the external non-dominated solution set ranked last. The statement coverage of all test cases in the dominant solution set, and the effective execution time of any test case in the external non-dominated solution set ranked first in the non-dominated sorted external non-dominated solution set series is less than that of the later sorted external non-dominated solution set. All test cases in the outer non-dominated solution set.

Specifically, for any two test case sequences R _k and R _r , when APFD _k >APFD _r and APSC _k >APSC _r and EET _k >EET _r , or APFD _k < APFD _r and APSC _k < APSC _r and EET When _k < EET _r , or APFD _k >APFD _r and APSC _k < APSC _r and EET _k < EET _r , or APFD _k < APFD _r and APSC _k >APSC _r and EET _k >EET _r , or APFD _k < When APFD _r and APSC _k >APSC _r and EET _k <EET _r , or when APFD _k >APFD _r and APSC _k <APSC _r and EET _k >EET _r , R _k and R _r are mutually non-dominant solutions.

In at least one embodiment of the present application, the calculating the crowding degree of the combined population of the first-generation test case sequences includes:

Let the crowding degree of the test case sequence t in the first-generation test case sequence merged population d _t =0, where t represents the test case sequence, t=1, 2,...,n;

For each objective function f(x) do the following:

Based on the objective function f(x), single-objective sorting is performed on the test case sequences in the merged population of the first-generation test case sequences;

Let the crowding degree of the two test case sequences d ₁ and d _n be infinite, that is, d ₁ =d _n =∞;

The crowding degree of the test case sequence t is calculated according to the formula d _t =d _t +(f(t+1)-f(t-1)), where t=2,3,...,n-1.

In an embodiment of the present application, the first-generation test case sequence population of the first scale obtained by calculating the first-generation test case sequence merged population by using a crowding degree comparison operator according to the crowding degree includes:

Create a first-generation reserve population;

adding the external non-dominated solution sets in the non-dominated sorted external non-dominated solution set series to the first-generation preliminary population in order, until the first-generation preliminary population size is greater than or equal to the first size; or

When the size of the first-generation preliminary population is equal to the first size, use the first-generation preliminary population as the first-generation test case sequence population; or

When the scale of the first-generation preliminary population is larger than the first scale, use the crowding degree comparison operator to sort the first-generation preliminary population according to the crowding degree to obtain the first-generation test of the first scale Use case sequence population.

In at least one embodiment of the present application, using the crowding degree comparison operator to sort the first-generation preliminary population includes:

For the two test case sequences i and j in the first-generation preliminary population, when the outer non-dominated solution set where the test case sequence i is located is better than the outer non-dominated solution set where the test case sequence j is located, the test Case sequence i is ranked better than test case sequence j; or

When test case sequence i and test case sequence j are in the same external non-dominated solution set and the crowding degree of test case sequence i is greater than that of test case sequence j, the ordering of test case sequence i is better than that of test case sequence j.

Step S15: Perform a preset operation on the first-generation test case sequence population until g is equal to a preset threshold, take the g-th generation test case sequence population as the optimal solution set, and output the optimal solution set.

In this embodiment, the performing a preset operation includes:

Use the selection function to evaluate the g-th generation test case sequence population to obtain the evaluation value of the test case sequence, and perform random selection without playback on the g-th generation test case sequence population according to the evaluation value of each test case sequence, and use a single point The crossover operator and/or the improved mutation operator calculate the test case sequence obtained by random selection without playback to obtain the g+1 generation offspring test case sequence population;

combining the g-th generation test case sequence population and the g+1-th generation progeny test-case sequence population to obtain a second-scale combined population of the g+1-th generation test case sequence;

Perform non-dominated sorting on the combined population of the g+1 generation test case sequence;

Calculate the crowding degree of the combined population of the g+1 generation test case sequence;

According to the crowding degree of the combined population of the g+1-th generation test case sequence, the crowding degree comparison operator is used to obtain the first-scale g+1-th generation test case sequence population, and the number of iterations g=g+1.

Using the improved mutation operator to obtain the first-scale g+1-th generation test case sequence population according to the first-scale g-th generation test case sequence population includes:

Set gen1, where 1<gen1<n/2, set gen2, where gen1<gen2<n, n is the first scale;

Set the high-order region of the test case sequence in the g-th generation test case sequence population to [0, length/3], the median region of the test case sequence to be (length/3, length*2/3], and the test case sequence The low-order area of is (length*2/3, length], and length is the length of the test case sequence;

When 1≤g<gen1, randomly select at least one test case sequence in the g-th generation test case sequence population, and randomly select each test case sequence in the at least one test case sequence Using the two loci in the high order region of the case sequence, the values of the two loci are exchanged;

When gen1≤g<gen2, randomly select at least one test case sequence in the g-th generation test case sequence population, and randomly select each test case sequence in the at least one test case sequence Using the two loci of the median region of the sequence of the case, the values of the two loci are exchanged;

When gen2≤g<n, randomly select at least one test case sequence in the g-th generation test case sequence population, and randomly select each test case sequence in the at least one test case sequence Using two loci in the lower region of the case sequence, the values of the two loci are swapped.

It should be noted that, in order to ensure the privacy and security of the data and output results in the above processing process, the data and output results in the processing process can be stored in the blockchain, such as the test case set, the The primary test case sequence population, the progeny test case sequence population, the optimal test case sequence, etc.

In this application, initialize the test case set to obtain the initial test case sequence population, encode the initial test case sequence population, use the selection function to evaluate the test case sequence in the initial test case sequence population, use the selection, crossover and/or sequence number mutation method to obtain The descendant test case sequence population, merge the descendant test case population and the original test case population to obtain the combined population, sort the population, and use the crowding degree comparison operator to obtain the next generation population, through cyclic selection, crossover and/or according to the number of iterations The method of mutating the test case sequence and using the non-dominated sorting genetic algorithm based on Pareto optimality and the crowding degree comparison operator to obtain the optimal solution set, and output the optimal test case sequence. Dominance sorting improves the efficiency of software fault detection and regression testing, thereby reducing software testing costs.

Example 2

FIG. 2 is a structural diagram of a test case sorting apparatus 30 in an embodiment of the present application.

In some embodiments, the test case sequencing apparatus 30 runs in an electronic device. The test case sequencing apparatus 30 may include a plurality of functional modules composed of program code segments. The program codes of each program segment in the test case sequencing apparatus 30 may be stored in a memory and executed by at least one processor to perform a test case sequencing function.

In this embodiment, the test case sorting apparatus 30 may be divided into a plurality of functional modules according to the functions performed by the test case sorting apparatus 30 . Referring to FIG. 2 , the test case sorting apparatus 30 may include a population initialization module 301 , an encoding module 302 , a merging module 303 , a sorting module 304 and an output module 305 . A module referred to in this application refers to a series of computer-readable instruction segments that can be executed by at least one processor and can perform fixed functions, and are stored in a memory. The functions of each module will be described in detail in subsequent embodiments.

The population initialization module 301 obtains a test case set to obtain a test case set, arranges the test cases in the test case set in random order to obtain a first-scale test case sequence, and uses the first-scale test case sequence as the first-generation test case. sequence population.

In at least one embodiment of the present application, the size of the primary test case sequence population is determined according to the number of test cases in the test case set.

Specifically, the test case set is composed of test cases, the test case sequence is obtained by arranging the test cases in the test case set in random order, and the test case sequence population is composed of test case sequences.

The encoding module 302 performs binary encoding on each test case sequence in the population of primary test case sequences.

In at least one embodiment of the present application, the binary encoding of each test case sequence in the primary test case sequence population includes:

The test case sequence in the primary test case sequence population is encoded according to the locus of the test case sequence in a binary coding manner, and the locus of the test case sequence is the serial number of the corresponding test case.

The merging module 303 uses a selection function to evaluate the binary-coded first-generation test case sequence population to obtain an evaluation value of the test case sequence, and performs no playback on the first-generation test case sequence population according to the evaluation value of each test case sequence. Randomly select, and use the single-point crossover operator and/or the sequence number mutation operator to calculate the test case sequence obtained by random selection without playback to obtain the first-generation progeny test case sequence population, and combine the first-generation progeny test cases. The sequence population is combined with the primary test case sequence population to obtain a second-scale merged population of primary test case sequences.

In at least one embodiment of the present application, the merging module 303 uses a selection function to evaluate the binary-coded first-generation test case sequence population to obtain an evaluation value of the test case sequence including:

According to the formula

Evaluate the population of the first-generation test case sequence of the first scale to obtain the evaluation value of the test case sequence

in,

represents the similarity of the test case sequence,

Represents the dissimilarity of the test case sequence;

The calculation method is

Among them, S(x _i ,x _j ) represents the similarity between x _i and x _j , and the calculation method is:

The calculation method is:

Among them, D(x _i , x _j ) represents the dissimilarity between x _i and x _j , and the calculation method is:

In at least one embodiment of the present application, α and β take a value of 0.5, and in other embodiments of the present application, α and β can take other values.

In at least one embodiment of the present application, the merging module 303 randomly selects the initial test case sequence population without playback according to the evaluation value of each test case sequence, and uses a single-point crossover operator and/or The sequence number mutation operator calculates the test case sequence obtained by random selection without playback to obtain the first-generation descendant test case sequence population including:

The creating step is to create the first-generation progeny test case sequence population;

In the random selection step, according to the evaluation value of each test case sequence, the initial resource pool is obtained by randomly selecting the population of the first-generation test case sequence without playback;

The crossover mutation step is to use the single-point crossover operator and/or the sequence number mutation operator to crossover and mutate the test case sequence in the initial resource pool, and add the crossover and mutated test case sequence to the test case sequence. Describe the descendant test case sequence population;

In the population generation step, the random selection step and the crossover mutation step are repeated until the population size of the progeny test case sequence is the first size.

In at least one embodiment of the present application, according to the initial test case sequence population, according to the evaluation value, using a random selection operator without playback to obtain the initial resource pool includes:

Calculate the expected survival number M of each test case sequence in the first generation test case sequence population in the next generation according to the formula M=f _i /∑f _i /n, where f _i is the objective function value of the test case sequence i, n is the size of the primary test case sequence population;

In the random selection step, according to the evaluation value, the test case sequence in the initial test case sequence population is randomly selected to enter the initial resource pool, if the test case sequence in the initial resource pool is selected and performed with the single-point crossover operator. operation, then the expected survival number M in the next generation of the test case sequence in the initial resource pool is minus 0.5, if the test case sequence in the initial resource pool is not selected and not related to the single point If the crossover operator is performed, the expected number of survival of the test case sequence in the initial resource pool in the next generation is subtracted by 1;

The random selection step is repeated until the expected survival number of the test case sequence in the initial resource pool is less than 0.

In at least one embodiment of the present application, when the survival expectation of the test case sequence in the initial resource pool is less than 0, the test case sequence in the initial resource pool is no longer selected.

In at least one embodiment of the present application, the objective function value is calculated by an objective function, and the objective function includes an average fault detection rate APFD (Average Percentage of Faults Detected), a statement coverage rate APSC (Average Percentage of Statement Coverage) , Effective execution time EET (Effective Execution Time).

In at least one embodiment of the present application, the average failure detection rate is based on the formula

Calculated, where APFD is the average failure detection rate, n is the number of test cases in the test case set T, m ₁ is the total number of software defects under test, and TF _i represents the first detection in the sequence of test cases T' after sorting The order in which the test cases for defect i are placed in this execution order.

In this embodiment, the average failure detection rate is used to calculate the average cumulative proportion of defects detected during the execution of the test case.

In at least one embodiment of the present application, the statement coverage is based on the formula

Calculated, where APSC is the statement coverage rate, n is the number of test cases in the test case set T, m ₂ is the total number of software statements under test, TS _i represents the first coverage of the i-th test case sequence T' after sorting The order in which the test cases of the line of code are placed in this execution order.

In this embodiment, the statement coverage rate is used to calculate the ratio of the number of executable statements to the number of all executable statements used when the test case is executed.

In at least one embodiment of the present application, the effective execution time is based on the formula

Calculated, wherein, EET is the effective execution time, and ET _i represents the time consumed when executing the i-th test case.

In this embodiment, the effective execution time is used to calculate the total execution time when the fault detection rate and the statement coverage rate of the test case reach the maximum.

In at least one embodiment of the present application, using a single-point crossover operator to cross the test cases in the initial resource pool includes:

randomly select at least one pair of parent test case sequences in the initial resource pool;

for each pair of parent test case sequences of the at least one pair of parent test case sequences, perform single-point crossover;

The single point intersection includes:

For a pair of parent test case sequences, the No. 1 parent test case sequence and the No. 2 parent test case sequence, the cross position k, k∈[0, L] is randomly selected, where L is the coding length of the population test case sequence, and the No. 1 parent test case sequence is The first k genes of the test case sequence are directly used as the first k genes of the No. 1 sub-test case sequence, and the same genes in the No. 2 parent test case sequence as the first k genes of the No. 1 parent test case sequence are removed to obtain a A new gene fragment of length L-k is used as the last L-k genes of the sub-test case sequence No. 1, and the same method is used to generate the sub-test case sequence No. 2.

In at least one embodiment of the present application, using the sequence number mutation operator to mutate the test case sequence in the initial resource pool includes:

Randomly select at least one test case sequence in the initial resource pool, for each test case sequence in the at least one test case sequence, randomly select two loci on the test case sequence, and combine the two loci of the test case sequence. value is exchanged.

The sorting module 304 performs non-dominant sorting on the combined population of the first-generation test case sequences, calculates the crowding degree of the combined population of the first-generation test case sequences, and uses a crowding degree comparison operator to perform a non-dominant sorting on the combined population of the first-generation test case sequences according to the crowding degree. The first-generation test case sequence is combined with the population for calculation to obtain the first-scale first-generation test case sequence population, and the number of iterations g=1.

In at least one embodiment of the present application, the non-dominated sorting of the first-generation test case sequence merged population includes:

The calculation step is to calculate the fault detection rate, statement coverage rate and effective execution time of all test case sequences in the population, and use the fault detection rate, the statement coverage rate and the effective execution time as a sorting objective function, Get three arrays of objective function values;

A sorting step, creating an external non-dominated solution set, with the goal of maximizing the fault detection rate, maximizing the statement coverage, and minimizing the effective execution time, according to the three arrays consisting of objective function values, Obtain paired non-dominated solutions, add the paired non-dominated solutions to the external non-dominated solution set, compare the test case sequences in the external non-dominated solution set pairwise, and remove the solutions that are not non-dominated solutions, so that The test case sequences in the external non-dominated solution set are mutually non-dominated solutions, wherein the non-dominated solutions are test case sequences of non-dominated relationships;

The solution set generation step is to perform the calculation step and the sorting step on all test case sequences in the merged population of the first-generation test case sequences except the test case sequences in the external non-dominated solution set, to obtain a non-dominated ranking The subsequent external non-dominated solution set series, the failure detection rate of any test case in the external non-dominated solution set ranked first in the non-dominated sorted external non-dominated solution set series is greater than that of the external non-dominated solution set after the ranking. The failure detection rate of all test cases in the set, and the statement coverage rate of any test case in the external non-dominated solution set ranked first in the external non-dominated solution set series after the non-dominated sorting is greater than that of the external non-dominated solution set ranked last. The statement coverage of all test cases in the dominant solution set, and the effective execution time of any test case in the external non-dominated solution set ranked first in the non-dominated sorted external non-dominated solution set series is less than that of the later sorted external non-dominated solution set. All test cases in the outer non-dominated solution set.

Specifically, the non-dominated sorting for any two test case sequences R _k and R _r includes:

When APFD _k >APFD _r and APSC _k >APSC _r and EET _k >EET _r , or APFD _k < APFD _r and APSC _k < APSC _r and EET _k < EET _r , or APFD _k >APFD _r and APSC _k < APSC _r and EET _k < EET _r , or APFD _k < APFD _r and APSC _k >APSC _r and EET _k >EET _r , or APFD _k < APFD _r and APSC _k >APSC _r and EET _k < EET _r , Or when APFD _k >APFD _r and APSC _k <APSC _r and EET _k >EET _r , R _k and R _r are mutually non-dominated solutions.

In at least one embodiment of the present application, the calculating the crowding degree of the combined population of the first-generation test case sequences includes:

Let the crowding degree of the test case sequence t in the first-generation test case sequence merged population d _t =0, where t represents the test case sequence, t=1, 2,...,n;

For each objective function f(x) do the following:

Based on the objective function f(x), single-objective sorting is performed on the test case sequences in the merged population of the first-generation test case sequences;

Let the crowding degree of the two test case sequences d ₁ and d _n be infinite, that is, d ₁ =d _n =∞;

The crowding degree of the test case sequence t is calculated according to the formula d _t =d _t +(f(f+1)-f(t-1)), where t=2,3,...,n-1.

In an embodiment of the present application, the first-generation test case sequence population of the first scale obtained by calculating the first-generation test case sequence merged population by using a crowding degree comparison operator according to the crowding degree includes:

Create a first-generation reserve population;

adding the external non-dominated solution sets in the non-dominated sorted external non-dominated solution set series to the first-generation preliminary population in order, until the first-generation preliminary population size is greater than or equal to the first size; or

When the size of the first-generation preliminary population is equal to the first size, use the first-generation preliminary population as the first-generation test case sequence population; or

When the scale of the first-generation preliminary population is larger than the first scale, use the crowding degree comparison operator to sort the first-generation preliminary population according to the crowding degree to obtain the first-generation test of the first scale Use case sequence population.

In at least one embodiment of the present application, using the crowding degree comparison operator to sort the first-generation preliminary population includes:

For the two test case sequences i and j in the first-generation preliminary population, when the outer non-dominated solution set where the test case sequence i is located is better than the outer non-dominated solution set where the test case sequence j is located, the test Case sequence i is ranked better than test case sequence j; or

When test case sequence i and test case sequence j are located in the same external non-dominated solution set and the crowding degree of test case sequence i is greater than that of test case sequence j, the ordering of test case sequence i is better than that of test case sequence j.

The output module 305 performs a preset operation on the first-generation test case sequence population until g is equal to a preset threshold, takes the g-th generation test case sequence population as the optimal solution set, and outputs the optimal solution set.

In this embodiment, the performing a preset operation includes:

Use the selection function to evaluate the g-th generation test case sequence population to obtain the evaluation value of the test case sequence, and perform random selection without playback on the g-th generation test case sequence population according to the evaluation value of each test case sequence, and use a single point The crossover operator and/or the improved mutation operator calculate the test case sequence obtained by random selection without playback to obtain the g+1 generation offspring test case sequence population;

combining the g-th generation test case sequence population and the g+1-th generation progeny test case sequence population to obtain a second-scale combined population of the g+1-th generation test case sequence;

Perform non-dominated sorting on the combined population of the g+1 generation test case sequence;

Calculate the crowding degree of the combined population of the g+1 generation test case sequence;

According to the crowding degree of the combined population of the test case sequence of the g+1 generation, the crowding degree comparison operator is used to obtain the test case sequence population of the g+1 generation of the first scale, and the number of iterations is g=g+1.

Using the improved mutation operator to obtain the first-scale g+1-th generation test case sequence population according to the first-scale g-th generation test case sequence population includes:

Set gen1, where 1<gen1<n/2, set gen2, where gen1<gen2<n, n is the first scale;

Set the high-order region of the test case sequence in the g-th generation test case sequence population as [0, length/3], the median region of the test case sequence as (length/3, length*2/3], and the test case sequence The low-order area of is (length*2/3, length], and length is the length of the test case sequence;

When 1≤g<gen1, randomly select at least one test case sequence in the g-th generation test case sequence population, and randomly select each test case sequence in the at least one test case sequence Using two loci in the high order region of the case sequence, the values of the two loci are exchanged;

When gen1≤g<gen2, randomly select at least one test case sequence in the g-th generation test case sequence population, and randomly select each test case sequence in the at least one test case sequence Using the two loci of the median region of the case sequence, the values of the two loci are exchanged;

When gen2≤g<n, randomly select at least one test case sequence in the g-th generation test case sequence population, and randomly select each test case sequence in the at least one test case sequence Using two loci in the lower region of the case sequence, the values of the two loci are swapped.

In this application, initialize the test case set to obtain the initial test case sequence population, encode the initial test case sequence population, use the selection function to evaluate the test case sequence in the initial test case sequence population, and use the selection, crossover and/or sequence number mutation method to obtain The descendant test case sequence population, merge the descendant test case population and the original test case population to obtain the combined population, sort the population, and use the crowding degree comparison operator to obtain the next generation population, through cyclic selection, crossover and/or according to the number of iterations The method of mutating the test case sequence and using the non-dominated sorting genetic algorithm based on Pareto optimality and the crowding degree comparison operator to obtain the optimal solution set, and output the optimal test case. Sorting improves the efficiency of software fault detection and regression testing, thereby reducing software testing costs.

Example 3

FIG. 3 is a schematic diagram of an electronic device 6 in an embodiment of the present application.

The electronic device 6 includes a memory 61 , a processor 62 and computer readable instructions stored in the memory 61 and executable on the processor 62 . When the processor 62 executes the computer-readable instructions, the steps in the above embodiment of the test case sorting method are implemented, for example, steps S11 to S15 shown in FIG. 1 . Alternatively, when the processor 62 executes the computer-readable instructions, the functions of each module/unit in the above-mentioned embodiment of the apparatus for sorting test cases, such as modules 301 to 305 in FIG. 2 , are implemented.

Exemplarily, the computer-readable instructions may be divided into one or more modules/units, and the one or more modules/units are stored in the memory 61 and executed by the processor 62 to Complete this application. The one or more modules/units may be a series of computer-readable instruction segments capable of accomplishing specific functions, and the instruction segments are used to describe the execution process of the computer-readable instructions in the electronic device 6 . For example, the computer-readable instructions can be divided into a population initialization module 301, an encoding module 302, a merging module 303, a sorting module 304, and an output module 305 in FIG.

In this embodiment, the electronic device 6 may be a computing device such as a desktop computer, a notebook, a palmtop computer, a server, and a cloud terminal device. Those skilled in the art can understand that the schematic diagram is only an example of the electronic device 6, and does not constitute a limitation to the electronic device 6, and may include more or less components than the one shown, or combine some components, or different Components such as the electronic device 6 may also include input and output devices, network access devices, buses, and the like.

The so-called processor 62 may be a central processing module (Central Processing Unit, CPU), or other general-purpose processors, digital signal processors (Digital Signal Processor, DSP), application specific integrated circuits (Application Specific Integrated Circuit, ASIC), Field-Programmable Gate Array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or the processor 62 can also be any conventional processor, etc. The processor 62 is the control center of the electronic device 6, and uses various interfaces and lines to connect the entire electronic device 6. of each part.

The memory 61 may be used to store the computer-readable instructions and/or modules/units, and the processor 62 executes or executes the computer-readable instructions and/or modules/units stored in the memory 61, and calls The data stored in the memory 61 realizes various functions of the electronic device 6 . The memory 61 may mainly include a stored program area and a stored data area, wherein the stored program area may store an operating system, an application program required for at least one function (such as a sound playback function, an image playback function, etc.); the storage data area may Data and the like created according to the use of the electronic device 6 are stored. In addition, the memory 61 may include volatile memory, and may also include non-volatile memory, such as hard disk, internal memory, plug-in hard disk, Smart Media Card (SMC), Secure Digital (SD) card , a flash memory card (Flash Card), at least one disk storage device, flash memory device, or other storage device.

If the modules/units integrated in the electronic device 6 are implemented in the form of software functional modules and sold or used as independent products, they may be stored in a computer-readable storage medium. Based on this understanding, the present application can implement all or part of the processes in the methods of the above embodiments, and can also be completed by instructing relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. , the computer-readable instructions, when executed by the processor, can implement the steps of the above-mentioned method embodiments. The computer-readable storage medium may be a non-volatile storage medium or a volatile storage medium. The computer-readable instructions include computer-readable instruction code, which may be in source code form, object code form, executable file, or some intermediate form, and the like.

The blockchain referred to in this application is a new application mode of computer technologies such as distributed data storage, point-to-point transmission, consensus mechanism, and encryption algorithm. Blockchain, essentially a decentralized database, is a series of data blocks associated with cryptographic methods. Each data block contains a batch of network transaction information to verify its Validity of information (anti-counterfeiting) and generation of the next block. The blockchain can include the underlying platform of the blockchain, the platform product service layer, and the application service layer.

In the several embodiments provided in this application, it should be understood that the disclosed apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are only illustrative. For example, the division of the modules is only a logical function division, and there may be other division manners in actual implementation.

In addition, each functional module in each embodiment of the present application may be integrated in the same processing module, or each module may exist physically alone, or two or more modules may be integrated in the same module. The above-mentioned integrated modules can be implemented in the form of hardware, or can be implemented in the form of hardware plus software function modules.

It will be apparent to those skilled in the art that the present application is not limited to the details of the above-described exemplary embodiments, but that the present application can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application. Accordingly, the embodiments are to be regarded in all respects as illustrative and not restrictive, and the scope of the application is to be defined by the appended claims rather than the foregoing description, which is therefore intended to fall within the scope of the claims. All changes within the meaning and scope of the equivalents of , are included in this application. Any reference signs in the claims shall not be construed as limiting the involved claim. Furthermore, it is clear that the word "comprising" does not exclude other modules or steps, and the singular does not exclude the plural. Multiple modules or electronic devices stated in this application may also be implemented by the same module or electronic device through software or hardware. The terms first, second, etc. are used to denote names and do not denote any particular order.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application rather than limitations. Although the present application has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application can be Modifications or equivalent substitutions can be made without departing from the spirit and scope of the technical solutions of the present application.

Claims (20)

1. A test case sorting method, wherein the test case sorting method comprises:

   Obtaining a test case set, arranging the test cases in the test case set in random order to obtain a first-scale test case sequence, and using the first-scale test case sequence as a population of first-generation test case sequences;

   performing binary coding on each test case sequence in the primary test case sequence population;

   Use a selection function to evaluate the binary-coded primary test case sequence population to obtain the evaluation value of the test case sequence, and perform random selection without playback on the primary test case sequence population according to the evaluation value of each test case sequence, and use The single-point crossover operator and/or the sequence number mutation operator calculates the test case sequence obtained by random selection without playback to obtain the first-generation progeny test case sequence population, and combines the first-generation progeny test case sequence population with the The first-generation test case sequence population obtains the second-scale first-generation test case sequence merged population;

   Perform non-dominated sorting on the combined population of the first-generation test case sequences, calculate the crowding degree of the combined population of the first-generation test case sequences, and use a crowding degree comparison operator to test the first generation according to the crowding degree The use case sequence is combined with the population for calculation to obtain the first-generation test case sequence population of the first scale, and the number of iterations g=1;

   A preset operation is performed on the first-generation test case sequence population until g is equal to a preset threshold, and the g-th generation test case sequence population is used as the optimal solution set, and the optimal solution set is output.
2. The method for sorting test cases according to claim 1, wherein the performing a preset operation comprises:

   Use the selection function to evaluate the g-th generation test case sequence population to obtain the evaluation value of the test case sequence, and perform random selection without playback on the g-th generation test case sequence population according to the evaluation value of each test case sequence, and use a single point The crossover operator and/or the improved mutation operator calculate the test case sequence obtained by random selection without playback to obtain the g+1 generation offspring test case sequence population;

   combining the g-th generation test case sequence population and the g+1-th generation progeny test case sequence population to obtain a second-scale combined population of the g+1-th generation test case sequence;

   Perform non-dominated sorting on the combined population of the g+1 generation test case sequence;

   Calculate the crowding degree of the combined population of the g+1 generation test case sequence;

   According to the crowding degree of the combined population of the test case sequence of the g+1 generation, the crowding degree comparison operator is used to obtain the test case sequence population of the g+1 generation of the first scale, and the number of iterations is g=g+1.
3. The test case sorting method according to claim 1, wherein the first-generation test case sequence population is randomly selected without playback according to the evaluation value of each test case sequence, and a single-point crossover operator and/or serial number are used. The mutation operator calculates the test case sequence obtained by random selection without playback to obtain the first-generation descendant test case sequence population including:

   The creating step is to create the first-generation progeny test case sequence population;

   In the random selection step, according to the evaluation value of each test case sequence, the initial resource pool is obtained by randomly selecting the population of the first-generation test case sequence without playback;

   The crossover mutation step is to use the single-point crossover operator and/or the sequence number mutation operator to crossover and mutate the test case sequence in the initial resource pool, and add the crossover and mutated test case sequence to the test case sequence. Describe the descendant test case sequence population;

   In the population generation step, the random selection step and the crossover mutation step are repeated until the population size of the progeny test case sequence is the first size.
4. The test case sorting method according to claim 3, wherein using the sequence number mutation operator to mutate the test case sequence in the initial resource pool comprises:

   Randomly select at least one test case sequence in the initial resource pool, for each test case sequence in the at least one test case sequence, randomly select two loci on the test case sequence, and exchange two of the genes Bit corresponding to the test case.
5. The test case sorting method according to claim 1, wherein the non-dominated sorting of the first-generation test case sequence merged population comprises:

   The calculation step is to calculate the fault detection rate, statement coverage rate and effective execution time of all test case sequences in the population, and use the fault detection rate, the statement coverage rate and the effective execution time as a sorting objective function, Get three arrays of objective function values;

   A sorting step, creating an external non-dominated solution set, with the goal of maximizing the fault detection rate, maximizing the statement coverage, and minimizing the effective execution time, according to the three arrays consisting of objective function values, Obtain paired non-dominated solutions, add the paired non-dominated solutions to the external non-dominated solution set, compare the test case sequences in the external non-dominated solution set pairwise, and remove the solutions that are not non-dominated solutions, so that The test case sequences in the external non-dominated solution set are mutually non-dominated solutions, wherein the non-dominated solutions are test case sequences of non-dominated relationships;

   The solution set generation step is to perform the calculation step and the sorting step on all test case sequences in the merged population of the first-generation test case sequences except the test case sequences in the external non-dominated solution set, to obtain a non-dominated ranking The subsequent external non-dominated solution set series, the failure detection rate of any test case in the external non-dominated solution set ranked first in the non-dominated sorted external non-dominated solution set series is greater than that of the external non-dominated solution set after the ranking. The failure detection rate of all test cases in the set, and the statement coverage rate of any test case in the external non-dominated solution set ranked first in the external non-dominated solution set series after the non-dominated sorting is greater than that of the external non-dominated solution set ranked last. The statement coverage of all test cases in the dominant solution set, and the effective execution time of any test case in the external non-dominated solution set ranked first in the non-dominated sorted external non-dominated solution set series is less than that of the later sorted external non-dominated solution set. All test cases in the outer non-dominated solution set.
6. The test case sorting method according to claim 1, wherein the calculating the crowding degree of the merged population of the first-generation test case sequences comprises:

   Let the crowding degree of the test case sequence t in the first-generation test case sequence merged population d _t =0, where t represents the test case sequence, t=1, 2,...,n;

   For each objective function f(x) do the following:

   Based on the objective function f(x), single-objective sorting is performed on the test case sequences in the merged population of the first-generation test case sequences;

   Let the crowding degree of the two test case sequences d ₁ and d _n be infinite, that is, d ₁ =d _n =∞;

   The crowding degree of the test case sequence t is calculated according to the formula d _t =d _t +(f(t+1)-f(t-1)), where t=2,3,...,n-1.
7. The test case sorting method according to claim 5, wherein the first generation test case of the first scale is obtained by calculating the merged population of the first generation test case sequence using a crowding degree comparison operator according to the congestion degree Sequence populations include:

   Create a first-generation reserve population;

   adding the external non-dominated solution sets in the non-dominated sorted external non-dominated solution set series to the first-generation preliminary population in order, until the first-generation preliminary population size is greater than or equal to the first size; or

   When the size of the first-generation preliminary population is equal to the first size, use the first-generation preliminary population as the first-generation test case sequence population; or

   When the scale of the first-generation preliminary population is larger than the first scale, use the crowding degree comparison operator to sort the first-generation preliminary population according to the crowding degree to obtain the first-generation test of the first scale Use case sequence population.
8. The method for sorting test cases according to claim 7, wherein sorting the first-generation preliminary population by using the crowding degree comparison operator comprises:

   For the two test case sequences i and j in the first-generation preliminary population, when the outer non-dominated solution set where the test case sequence i is located is better than the outer non-dominated solution set where the test case sequence j is located, the test Case sequence i is ranked better than test case sequence j; or

   When test case sequence i and test case sequence j are in the same external non-dominated solution set and the crowding degree of test case sequence i is greater than that of test case sequence j, the ordering of test case sequence i is better than that of test case sequence j.
9. The test case sorting method according to claim 2, wherein using the improved mutation operator to obtain the first-scale g+1-th generation test case sequence population according to the first-scale g-th generation test case sequence population comprises:

   Set gen1, where 1<gen1<n/2, set gen2, where gen1<gen2<n, n is the first scale;

   Set the high-order region of the test case sequence in the g-th generation test case sequence population as [0, length/3], the median region of the test case sequence as (length/3, length*2/3], and the test case sequence The low-order area of is (length*2/3, length], and length is the length of the test case sequence;

   When 1≤g<gen1, randomly select at least one test case sequence in the g-th generation test case sequence population, and randomly select each test case sequence in the at least one test case sequence Use any two loci in the high-order region of the case sequence, and exchange the values of the two loci;

   When gen1≤g<gen2, randomly select at least one test case sequence in the g-th generation test case sequence population, and randomly select each test case sequence in the at least one test case sequence Using any two loci in the median region of the case sequence, the values of the two loci are exchanged;

   When gen2≤g<n, randomly select at least one test case sequence in the g-th generation test case sequence population, and randomly select each test case sequence in the at least one test case sequence Using any two loci in the lower region of the case sequence, the values of the two loci are exchanged.
10. An electronic device, wherein the electronic device includes a processor and a memory, and the processor is configured to execute at least one computer-readable instruction stored in the memory to implement the following steps:

    Obtaining a test case set, arranging the test cases in the test case set in random order to obtain a first-scale test case sequence, and using the first-scale test case sequence as a population of first-generation test case sequences;

    performing binary coding on each test case sequence in the primary test case sequence population;

    Use a selection function to evaluate the binary-coded primary test case sequence population to obtain the evaluation value of the test case sequence, and perform random selection without playback on the primary test case sequence population according to the evaluation value of each test case sequence, and use The single-point crossover operator and/or the sequence number mutation operator calculates the test case sequence obtained by random selection without playback to obtain the first-generation progeny test case sequence population, and combines the first-generation progeny test case sequence population with the The first-generation test case sequence population obtains the second-scale first-generation test case sequence merged population;

    Perform non-dominated sorting on the combined population of the first-generation test case sequences, calculate the crowding degree of the combined population of the first-generation test case sequences, and use a crowding degree comparison operator to test the first generation according to the crowding degree The use case sequence is combined with the population for calculation to obtain the first-generation test case sequence population of the first scale, and the number of iterations g=1;

    A preset operation is performed on the first-generation test case sequence population until g is equal to a preset threshold, and the g-th generation test case sequence population is used as the optimal solution set, and the optimal solution set is output.
11. The electronic device of claim 10, wherein, when performing the preset operation, the processor executes the at least one computer-readable instruction to implement the following steps:

    Use the selection function to evaluate the g-th generation test case sequence population to obtain the evaluation value of the test case sequence, and perform random selection without playback on the g-th generation test case sequence population according to the evaluation value of each test case sequence, and use a single point The crossover operator and/or the improved mutation operator calculate the test case sequence obtained by random selection without playback to obtain the g+1 generation offspring test case sequence population;

    combining the g-th generation test case sequence population and the g+1-th generation progeny test case sequence population to obtain a second-scale combined population of the g+1-th generation test case sequence;

    Perform non-dominated sorting on the combined population of the g+1 generation test case sequence;

    Calculate the crowding degree of the combined population of the g+1 generation test case sequence;

    According to the crowding degree of the combined population of the test case sequence of the g+1 generation, the crowding degree comparison operator is used to obtain the test case sequence population of the g+1 generation of the first scale, and the number of iterations is g=g+1.
12. The electronic device according to claim 10, wherein in the said first-generation test case sequence population is randomly selected without playback according to the evaluation value of each test case sequence, and a single-point crossover operator and/or sequence number mutation are used When the operator calculates the test case sequence obtained by random selection without replay to obtain the population of the first-generation progeny test case sequence, the processor executes the at least one computer-readable instruction to realize the following steps:

    The creating step is to create the first-generation progeny test case sequence population;

    In the random selection step, according to the evaluation value of each test case sequence, the initial resource pool is obtained by randomly selecting the population of the first-generation test case sequence without playback;

    The crossover mutation step is to use the single-point crossover operator and/or the sequence number mutation operator to crossover and mutate the test case sequence in the initial resource pool, and add the crossover and mutated test case sequence to the test case sequence. Describe the descendant test case sequence population;

    In the population generation step, the random selection step and the crossover mutation step are repeated until the population size of the progeny test case sequence is the first size.
13. 13. The electronic device of claim 12, wherein, when using the sequence number mutation operator to mutate the sequence of test cases in the initial resource pool, the processor executes the at least one computer-readable instruction to achieve The following steps:

    Randomly select at least one test case sequence in the initial resource pool, for each test case sequence in the at least one test case sequence, randomly select two loci on the test case sequence, and exchange two of the genes Bit corresponding to the test case.
14. 11. The electronic device of claim 10, wherein, in the non-dominated sorting of the first generation test case sequence pooled population, the processor executes the at least one computer-readable instruction to implement the following steps:

    The calculation step is to calculate the fault detection rate, statement coverage rate and effective execution time of all test case sequences in the population, and use the fault detection rate, the statement coverage rate and the effective execution time as a sorting objective function, Get three arrays of objective function values;

    A sorting step, creating an external non-dominated solution set, with the goal of maximizing the fault detection rate, maximizing the statement coverage, and minimizing the effective execution time, according to the three arrays consisting of objective function values, Obtain paired non-dominated solutions, add the paired non-dominated solutions to the external non-dominated solution set, compare the test case sequences in the external non-dominated solution set pairwise, and remove the solutions that are not non-dominated solutions, so that The test case sequences in the external non-dominated solution set are mutually non-dominated solutions, wherein the non-dominated solutions are test case sequences of non-dominated relationships;

    The solution set generation step is to perform the calculation step and the sorting step on all test case sequences in the merged population of the first-generation test case sequences except the test case sequences in the external non-dominated solution set, to obtain a non-dominated ranking The subsequent external non-dominated solution set series, the failure detection rate of any test case in the external non-dominated solution set ranked first in the non-dominated sorted external non-dominated solution set series is greater than that of the external non-dominated solution set after the ranking. The failure detection rate of all test cases in the set, and the statement coverage rate of any test case in the external non-dominated solution set ranked first in the external non-dominated solution set series after the non-dominated sorting is greater than that of the external non-dominated solution set ranked last. The statement coverage of all test cases in the dominant solution set, and the effective execution time of any test case in the external non-dominated solution set ranked first in the non-dominated sorted external non-dominated solution set series is less than that of the later sorted external non-dominated solution set. All test cases in the outer non-dominated solution set.
15. 11. The electronic device of claim 10, wherein, in said calculating the crowding degree of said first generation test case sequence merged population, said processor executes said at least one computer-readable instruction to implement the following steps:

    Let the crowding degree of the test case sequence t in the first-generation test case sequence merged population d _t =0, where t represents the test case sequence, t=1, 2,...,n;

    For each objective function f(x) do the following:

    Based on the objective function f(x), single-objective sorting is performed on the test case sequences in the merged population of the first-generation test case sequences;

    Let the crowding degree of the two test case sequences d ₁ and d _n be infinite, that is, d ₁ =d _n =∞;

    The crowding degree of the test case sequence t is calculated according to the formula d _t =d _t +(f(t+1)-f(t-1)), where t=2,3,...,n-1.
16. The electronic device according to claim 14, wherein a first-generation test case sequence of a first scale is obtained by calculating the merged population of the first-generation test case sequences using a crowding degree comparison operator according to the crowding degree. During the population, the processor executes the at least one computer-readable instruction to implement the following steps:

    Create a first-generation reserve population;

    adding the external non-dominated solution sets in the non-dominated sorted external non-dominated solution set series to the first-generation preliminary population in order, until the first-generation preliminary population size is greater than or equal to the first size; or

    When the size of the first-generation preliminary population is equal to the first size, use the first-generation preliminary population as the first-generation test case sequence population; or

    When the scale of the first-generation preliminary population is larger than the first scale, use the crowding degree comparison operator to sort the first-generation preliminary population according to the crowding degree to obtain the first-generation test of the first scale Use case sequence population.
17. 17. The electronic device of claim 16, wherein, in ordering the first generation preliminary population using the crowding comparison operator, the processor executes the at least one computer-readable instruction to implement the following steps :

    For the two test case sequences i and j in the first-generation preliminary population, when the outer non-dominated solution set where the test case sequence i is located is better than the outer non-dominated solution set where the test case sequence j is located, the test Case sequence i is ranked better than test case sequence j; or

    When test case sequence i and test case sequence j are in the same external non-dominated solution set and the crowding degree of test case sequence i is greater than that of test case sequence j, the ordering of test case sequence i is better than that of test case sequence j.
18. The electronic device according to claim 11, wherein when using the improved mutation operator to obtain the first-scale g+1-th generation test case sequence population according to the first-scale g-th generation test case sequence population, the The processor executes the at least one computer-readable instruction to implement the following steps:

    Set gen1, where 1<gen1<n/2, set gen2, where gen1<gen2<n, n is the first scale;

    Set the high-order region of the test case sequence in the g-th generation test case sequence population as [0, length/3], the median region of the test case sequence as (length/3, length*2/3], and the test case sequence The low-order area of is (length*2/3, length], and length is the length of the test case sequence;

    When 1≤g<gen1, randomly select at least one test case sequence in the g-th generation test case sequence population, and randomly select each test case sequence in the at least one test case sequence Use any two loci in the high-order region of the case sequence, and exchange the values of the two loci;

    When gen1≤g<gen2, randomly select at least one test case sequence in the g-th generation test case sequence population, and randomly select each test case sequence in the at least one test case sequence Using any two loci in the median region of the case sequence, the values of the two loci are exchanged;

    When gen2≤g<n, randomly select at least one test case sequence in the g-th generation test case sequence population, and randomly select each test case sequence in the at least one test case sequence Using any two loci in the lower region of the case sequence, the values of the two loci are exchanged.
19. A computer-readable storage medium, wherein the computer-readable storage medium stores at least one computer-readable instruction, and the at least one computer-readable instruction implements the following steps when executed by a processor:

    Obtaining a test case set, arranging the test cases in the test case set in random order to obtain a first-scale test case sequence, and using the first-scale test case sequence as a population of first-generation test case sequences;

    performing binary coding on each test case sequence in the primary test case sequence population;

    Use a selection function to evaluate the binary-coded primary test case sequence population to obtain the evaluation value of the test case sequence, and perform random selection without playback on the primary test case sequence population according to the evaluation value of each test case sequence, and use The single-point crossover operator and/or the sequence number mutation operator calculates the test case sequence obtained by random selection without playback to obtain the first-generation progeny test case sequence population, and combines the first-generation progeny test case sequence population with the The first-generation test case sequence population obtains the second-scale first-generation test case sequence merged population;

    Perform non-dominated sorting on the combined population of the first-generation test case sequences, calculate the crowding degree of the combined population of the first-generation test case sequences, and use a crowding degree comparison operator to test the first generation according to the crowding degree The use case sequence is combined with the population for calculation to obtain the first-generation test case sequence population of the first scale, and the number of iterations g=1;

    A preset operation is performed on the first-generation test case sequence population until g is equal to a preset threshold, and the g-th generation test case sequence population is used as the optimal solution set, and the optimal solution set is output.
20. An apparatus for sorting test cases, wherein the apparatus for sorting test cases includes:

    a population initialization module, used for acquiring a test case set, arranging the test cases in the test case set in random order to obtain a first-scale test case sequence, and using the first-scale test case sequence as the first-generation test case sequence population;

    an encoding module, configured to perform binary encoding on each test case sequence in the primary test case sequence population;

    The merging module is configured to use the selection function to evaluate the binary-coded population of the first-generation test case sequences to obtain the evaluation value of the test case sequence, and perform no playback on the population of the first-generation test case sequence according to the evaluation value of each test case sequence Randomly select, and use the single-point crossover operator and/or the sequence number mutation operator to calculate the test case sequence obtained by random selection without playback to obtain the first-generation progeny test case sequence population, and combine the first-generation progeny test cases. The sequence population and the first-generation test case sequence population obtain a second-scale first-generation test case sequence merged population;

    The sorting module is configured to perform non-dominated sorting on the combined population of the first-generation test case sequences, calculate the crowding degree of the combined population of the first-generation test case sequences, and use a crowding degree comparison operator to perform a sorting of all the combined populations according to the crowding degree. The first-generation test case sequence is combined with the population for calculation to obtain the first-scale first-generation test case sequence population, and the number of iterations g=1;

    The output module is configured to perform a preset operation on the first-generation test case sequence population until g is equal to a preset threshold value, take the g-th generation test case sequence population as the optimal solution set, and output the optimal solution set.

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