不可靠測(cè)試條件下基于NSGA-Ⅱ的多目標(biāo)測(cè)試優(yōu)化選擇

不可靠測(cè)試條件下基于NSGA-Ⅱ的多目標(biāo)測(cè)試優(yōu)化選擇AbstractTestingisacriticalcomponentofsoftwaredevelopment,anditisnecessarytoensurethatsoftwarefunctionsappropriatelyandmeetsuserexpectations.However,theresourcesneededtotestsoftwarearetime-consumingandexpensive,especiallywhenfacingthechallengeofoptimizingtestcasesunderunreliabletestingconditions.Thispaperproposesanapproachformulti-objectivetestoptimizationbasedonNSGA-Ⅱalgorithmunderunreliabletestingconditions.Thisapproachaimstoimprovetheefficiencyoftestingunderuncertainconditionstoobtainreliabletestingresultswithasmallernumberoftestcases.IntroductionTheprimarypurposeoftestingistoensurethatsoftwaremeetsusers’expectationsandhasnodefectsorerrors.However,softwaretestingisoftenexpensiveandrequiressignificantresources,includingtime,labor,andequipment.Inmanycases,testingisperformedunderuncertain,unreliableconditions,complicatingtheprocessevenfurther.Underthesecircumstances,itisnecessarytooptimizethetestcasesandimprovetheefficiencyoftesting.Toachievethisgoal,weproposeamulti-objectivetestoptimizationalgorithmbasedonNSGA-Ⅱunderunreliabletestingconditions.Theproposedapproachintegratestheadvantagesofmulti-objectiveoptimization,suchasParetooptimality,withthecapabilitiesofNSGA-Ⅱalgorithm,whichhasbeenwidelyusedinvariousoptimizationproblemsduetoitsgoodperformance.Theapproachaimstoreducethenumberoftestcases,maintainthedegreeoftestcoverage,andimprovetestingefficiencyunderuncertainconditionstoobtainmorereliabletestingresults.RelatedWorkManyresearchershaveproposedvariousmethodsforsoftwaretestingoptimization.Traditionalmethodsaimtoachievecompletecoverage,butthisoftenrequiresasignificantnumberoftestcases,whichistime-consumingandimpractical.Therefore,researchershavefocusedonreducingthenumberoftestcaseswhilestillmaintainingtestcoverage.Variousoptimizationalgorithmshavebeenproposedtoachievethisgoal,suchasgeneticalgorithms,particleswarmoptimizationalgorithms,andsimulatedannealingalgorithms.However,thesealgorithmsoftenhavelimitations,suchaspotentialprematureconvergenceorpoorperformanceinsolvinghigh-dimensionalproblems.NSGA-Ⅱalgorithmhasbecomeapopularalternativeforoptimizationproblemsduetoitshighefficiency,betterpopulationdiversity,andmulti-objectiveoptimizationcapabilities.Ithasalsobeenwidelyappliedinmanyfieldsandhasachievedsignificantresults.ProposedapproachTheproposedmulti-objectivetestoptimizationalgorithmbasedonNSGA-Ⅱconsistsofthefollowingsteps:1.Initializethepopulation:Thealgorithmstartsbycreatinganinitialpopulationoftestcasestobeoptimized.2.Generateanewpopulation:Next,anewpopulationisgeneratedfromtheinitialpopulationbasedontheNSGA-Ⅱalgorithm,whichincludesselection,crossover,andmutationoperations.3.Evaluatethefitnessofeachtestcase:Thenewpopulationoftestcasesisevaluatedtocalculatetheirfitnessvalueusingasetoffitnessfunctions.Inthiscase,fitnessfunctionsincludetestcoverage,executiontime,andfaultdetectionrate.4.Non-dominatedsortingandselection:TheNSGA-Ⅱalgorithmsortstestcasesintoseveralnon-dominatedfrontsbasedontheirfitnessvalueanddiversity.Thebesttestcaseisselectedfromthenon-dominatedfront.5.Terminationcriterion:Thealgorithmterminateswhentheterminationconditionismet,andtheselectedtestcasesareconsideredastheoptimaltestcases.ResultsandEvaluationTheproposedapproachwastestedondifferentsoftwareapplications,andtheresultsshowthatitcaneffectivelyoptimizetestcases,reducethenumberoftestcases,maintaintestcoverage,andimprovetestingefficiencyunderunreliabletestingconditions.Italsoperformsbetterthantraditionaltestingoptimizationalgorithms,suchasparticleswarmoptimizationalgorithmandgeneticalgorithm.ConclusionThispaperproposesanapproachformulti-objectivetestoptimizationbasedonNSGA-Ⅱalgorithmunderuncertaintestingconditions.Theapproachaimstoreducethenumberoftestcases,maintaintestcoverage,andimprovetestingefficiencytoobtainmorereliabletestingresults.Resultsshowtha