CN110084369A - Mutation testing variant reduction method based on multiple-objection optimization - Google Patents
Mutation testing variant reduction method based on multiple-objection optimization Download PDFInfo
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Abstract
The invention discloses a kind of mutation testing variant reduction method based on multiple-objection optimization, the technical issues of for solving existing variation body reduction method low efficiency.Technical solution is first to a large amount of variants of existing Program Generating, then the multi-objective optimization algorithm target that reduction is carried out to variant is formulated, including the time that variant is detected difference between rate, variant and source program, mutation testing is spent, about degeneracy is carried out to variant by different multi-objective optimization algorithms to record result one by one and compare, final choice shows optimal multi-objective optimization algorithm, it realizes the variant reduction based on multiple-objection optimization, improves the efficiency of variant reduction.
Description
Technical field
The present invention relates to a kind of variant reduction method, in particular to a kind of mutation testing variation based on multiple-objection optimization
Body reduction method.
Background technique
" Zeng Fanping, Huang Yuhan, Zhang Meichao wait to answer based on variant reduction [J] computer that genetic algorithm clusters to document
A kind of variant reduction method based on genetic algorithm cluster is disclosed with 2011,31 (5): 1314-1317. ".This method mentions
The variant reduction method based on genetic algorithm cluster is gone out, the variant with similar features has been placed in same cluster, then benefit
Genetic algorithm is used to randomly choose one from each cluster as representing, to realize the reduction to genetic algorithm variant.This article
It offers and only uses the reduction that same genetic algorithm carries out variant, there is no genetic algorithm is compared, do not prove to be made
The rationality that is involutory of genetic algorithm.
Summary of the invention
In order to overcome the shortcomings of existing variation body reduction method low efficiency, the present invention provides a kind of based on multiple-objection optimization
Mutation testing variant reduction method.This method to a large amount of variants of existing Program Generating, is then formulated to variant first
The multi-objective optimization algorithm target for carrying out reduction is detected difference between rate, variant and source program, variation including variant
The time spent is tested, about degeneracy is carried out to variant by different multi-objective optimization algorithms and records result one by one and carries out pair
Than final choice shows optimal multi-objective optimization algorithm, realizes the variant reduction based on multiple-objection optimization, improves variation
The efficiency of body reduction.
A kind of the technical solution adopted by the present invention to solve the technical problems: mutation testing variation based on multiple-objection optimization
Body reduction method, its main feature is that the following steps are included:
Step 1: being done for the procedure set of four open sources from software workpiece infrastructure library using what is generated at random
Method generates a large amount of variants.
First determine whether this variant had been generated when random generation variant, being generated then terminates
Process；Otherwise by insertion absolute value, replacement arithmetic operator, replacement logical operator, fallback relationship operator and insertion
The operation of n ary operation symbol, carries out the generation of variant, then removes variant of equal value, finally exports remaining variant and fortune
The time of calculation.
Step 2: being screened using existing six kinds of multi-objective optimization algorithms to variant.
6 kinds of widely used multi-objective optimization algorithms are selected as the object compared, be respectively NSGA-II, IBEA,
MOEA/D-WS, MOEA/D-TCH, MOEA/D-PBI and SPEA2+SDE.Three kinds of optimised mesh as evaluation index are formulated simultaneously
Mark is the difference of time, variant and source program needed for the tested extracting rate of variant, test respectively.Better variant collection
Conjunction should be smaller with source program difference, spends the time less, while being not easy to be detected by existing test use cases relatively, therefore
Wish all to minimize these three targets, calculation formula is as follows:
Wherein, D is the difference being mutated between program and source program, LmIt is the line number for being mutated program change, L is in source program
Line number.
Wherein, CostpIt is by standardized time-consuming, t is the time for running the disaggregation, tminIt is all disaggregation of operation
Minimum time, tmaxIt is the maximum time for running all disaggregation, since other two targets are indicated with percentage,
Standardization to spending the time to carry out percentage.
Wherein, RdThe tested extracting rate of variant, Pd' it is the variant number being detected, P ' is the variant sum generated.
Step 3: evaluating the variant set obtained after reduction, selects and be appropriate for the more of variant reduction
Objective optimization algorithm.
It is gone forward side by side by given index with this six kinds of algorithms to be screened to the variant of generation and obtain final result
Row evaluation, evaluation index carry out in terms of two, including integrally carrying out evaluation to disaggregation and concentrating to the solution of all generations best
One evaluated.The method for evaluating whole disaggregation is HV value, i.e., solution concentrates individual and reference point to be enclosed in object space
At obtaining the volume of hypercube.Evaluation to best disaggregation is being ensured by tested extracting rate, variant and the source of variant
The difference control of program is selected in the smallest situation, tests required time the smallest optimum mutation body set, and is divided
Analysis is compared.Finally select the multi-objective optimization algorithm for being appropriate for variant reduction.
The beneficial effects of the present invention are: then this method is formulated first to a large amount of variants of existing Program Generating to change
Allosome carries out the multi-objective optimization algorithm target of reduction, including variant be detected difference between rate, variant and source program,
The time that mutation testing is spent carries out about degeneracy to variant by different multi-objective optimization algorithms and records result one by one to go forward side by side
Row comparison, final choice show optimal multi-objective optimization algorithm, realize the variant reduction based on multiple-objection optimization, improve
The efficiency of variant reduction.
The present invention is based on experimental datas, and existing genetic algorithm is compared, and have selected most suitable under prescribed conditions
The genetic algorithm for carrying out variant reduction is closed, and demonstrates the feasibility that the genetic algorithm carries out reduction.In an experiment, raw first
It the use of six kinds of different multi-objective optimization algorithms by this 500 variant reduction is 200 at 500 different variants,
Different variants has been obtained to combine.Compared in total six kinds of multi-objective optimization algorithms (NSGA-II, IBEA, MOEA/D-WS,
MOEA/D-TCH, MOEA/D-PBI, SPEA2+SDE), the variant set that algorithm is generated from several different angles respectively into
Go comparison:
1) for all variant set after algorithm reduction, disaggregation is judged using HV: multiple-objection optimization field
In, using HV to carry out judgement as index is a very common mode, it is used to indicate that solution concentrates individual and reference point to exist
It surrounds to obtain the volume of hypercube in object space.The value of HV is higher, then it is assumed that this disaggregation have better convergence,
Scalability and consistency, that is, be better disaggregation.Comparison result is referring to table 1.It referring to table 2 is come to each procedure set
Say optimal and suboptimum algorithm.
The standardization HV value of table 1
The optimal algorithm of 2 distinct program of table is evaluated
2) it selects the solution concentration generated from each to select one group of best variant set to compare, when variant quilt
When the difference (%) of verification and measurement ratio (%) and variant and source program is all controlled to minimum, cost is compared, is found best
Variant collection.It is the result of experiment referring to table 3- table 6.
3 tcas programmed test result of table
4 schedule programmed test result of table
5 tot-info programmed test result of table
6 gzip programmed test result of table
It is final to assert, under given optimal conditions, selects and be appropriate for the multi-objective optimization algorithm of variant reduction and be
MOEA/D-WS。
It elaborates with reference to the accompanying drawings and detailed description to the present invention.
Detailed description of the invention
Fig. 1 is the flow chart of the mutation testing variant reduction method the present invention is based on multiple-objection optimization.
Fig. 2 is the flow chart that variant is generated in Fig. 1.
Specific embodiment
Referring to Fig.1-2.The present invention is based on the mutation testing variant reduction method of multiple-objection optimization, specific step is as follows:
Step 1: being done for the procedure set of four open sources from software workpiece infrastructure library using what is generated at random
Method generates a large amount of variants.
First determine whether this variant had been generated when random generation variant, being generated then terminates
Process；Otherwise pass through operation (insertion absolute value, replacement arithmetic operator, the replacement logical operator, replacement to five kinds of operators
Relational operator, insertion n ary operation symbol) Lai Jinhang variant generation, then remove variant of equal value, finally output is remaining
Variant and operation time.
Step 2: being screened using existing six kinds of multi-objective optimization algorithms to variant.
6 kinds of widely used multi-objective optimization algorithms are selected as the object compared, be respectively NSGA-II, IBEA,
MOEA/D-WS, MOEA/D-TCH, MOEA/D-PBI and SPEA2+SDE.We have formulated three kinds of quilts as evaluation index simultaneously
Optimization aim is the difference of time, variant and source program needed for the tested extracting rate of variant, test respectively.After study
Think, better variant set should be smaller with source program difference, spends the time less, while being not easy relatively by existing survey
Examination set of uses case detects that, therefore, it is desirable to all minimize to these three targets, calculation formula is as follows:
Wherein D is the difference being mutated between program and source program, LmIt is the line number for being mutated program change, L is in source program
Line number.
Wherein CostpIt is by standardized time-consuming, t is the time for running the disaggregation, tminIt is to run all disaggregation most
Small time, tmaxIt is the maximum time for running all disaggregation, it is right since other two targets are indicated with percentage
The time is spent to carry out the standardization of percentage.
Wherein RdThe tested extracting rate of variant, P 'dIt is the variant number being detected, P ' is the variant sum generated.
Step 3: evaluating the variant set obtained after reduction, selects and be appropriate for the more of variant reduction
Objective optimization algorithm.
It is gone forward side by side by given index with this six kinds of algorithms to be screened to the variant of generation and obtain final result
Row evaluation, evaluation index carry out in terms of two, including integrally carrying out evaluation to disaggregation and concentrating to the solution of all generations best
One evaluated.The method for evaluating whole disaggregation is HV value, i.e., solution concentrates individual and reference point to be enclosed in object space
At obtaining the volume of hypercube.Evaluation to best disaggregation is being ensured by tested extracting rate, variant and the source of variant
The difference control of program is selected in the smallest situation, tests required time the smallest optimum mutation body set, and is divided
Analysis is compared.Finally select the multi-objective optimization algorithm for being appropriate for variant reduction.
Claims (1)
1. a kind of mutation testing variant reduction method based on multiple-objection optimization, it is characterised in that the following steps are included:
Step 1: for the procedure set of four open sources from software workpiece infrastructure library, it is raw using the method generated at random
At a large amount of variants；
First determine whether this variant had been generated when random generation variant, being generated then terminates to flow
Journey；Otherwise by insertion absolute value, replacement arithmetic operator, replacement logical operator, fallback relationship operator and insertion member
The operation of operator, carries out the generation of variant, then removes variant of equal value, finally exports remaining variant and operation
Time；
Step 2: being screened using existing six kinds of multi-objective optimization algorithms to variant；
It selects 6 kinds of widely used multi-objective optimization algorithms as the object compared, is NSGA-II, IBEA, MOEA/D- respectively
WS, MOEA/D-TCH, MOEA/D-PBI and SPEA2+SDE；Three kinds of optimised targets as evaluation index are formulated simultaneously, respectively
It is the difference of time, variant and source program needed for the tested extracting rate of variant, test；Better variant set should be with
Source program difference is smaller, spends the time less, while being not easy to detect that therefore, it is desirable to this by existing test use cases relatively
Three targets are all minimized, and calculation formula is as follows:
Wherein, D is the difference being mutated between program and source program, LmIt is the line number for being mutated program change, L is the row in source program
Number；
Wherein, CostpIt is by standardized time-consuming, t is the time for running the disaggregation, tminIt is when running the minimum of all disaggregation
Between, tmaxIt is the maximum time for running all disaggregation, since other two targets are indicated with percentage, to cost
The standardization of time progress percentage；
Wherein, RdThe tested extracting rate of variant, P 'dIt is the variant number being detected, P ' is the variant sum generated；
Step 3: evaluating the variant set obtained after reduction, the multiple target for being appropriate for variant reduction is selected
Optimization algorithm；
Final result is screened and obtained to the variant of generation with this six kinds of algorithms by given index and is commented
Valence, evaluation index carry out in terms of two, including integrally carrying out evaluation to disaggregation and concentrating best one to the solution of all generations
It is a to be evaluated；The method for evaluating whole disaggregation is HV value, i.e., solution concentrates individual and reference point to surround in object space
To the volume of hypercube；Evaluation to best disaggregation is being ensured by tested extracting rate, variant and the source program of variant
Difference control select, test in the smallest situation needed for time the smallest optimum mutation body set, and carry out analysis ratio
Compared with；Finally select the multi-objective optimization algorithm for being appropriate for variant reduction.
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| CN114816982A (en) * | 2022-03-02 | 2022-07-29 | 南京航空航天大学 | SPEA 2-based RESTful API test suite minimization method in unmanned aerial vehicle PX4 |
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