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1、ExpertSystemswithApplications36(2009)7346–7354ContentslistsavailableatScienceDirectExpertSystemswithApplicationsjournalhomepage:www.elsevier.com/locate/eswaReviewAsystematicreviewofsoftwarefaultpredictionstudiesa,*bCagatayCatal,BanuDiriaTheScienti?candTechnologicalRe

2、searchCouncilofTurkey,MarmaraResearchCenter,InformationTechnologiesInstitute,Kocaeli,TurkeybYildizTechnicalUniversity,DepartmentofComputerEngineering,Istanbul,TurkeyarticleinfoabstractKeywords:Thispaperprovidesasystematicreviewofprevioussoftwarefaultpredictionstudies

3、withaspeci?cfocusMachinelearningonmetrics,methods,anddatasets.Thereviewuses74softwarefaultpredictionpapersin11journalsandAutomatedfaultpredictionmodelsseveralconferenceproceedings.Accordingtothereviewresults,theusagepercentageofpublicdatasetsPublicdatasetsincreasedsi

4、gni?cantlyandtheusagepercentageofmachinelearningalgorithmsincreasedslightlysinceMethod-levelmetrics2005.Inaddition,method-levelmetricsarestillthemostdominantmetricsinfaultpredictionresearchExpertsystemsareaandmachinelearningalgorithmsarestillthemostpopularmethodsforf

5、aultprediction.Researchersworkingonsoftwarefaultpredictionareashouldcontinuetousepublicdatasetsandmachinelearningalgorithmstobuildbetterfaultpredictors.Theusagepercentageofclass-levelisbeyondacceptablelevelsandtheyshouldbeusedmuchmorethantheyarenowinordertopredictthe

6、faultsearlierindesignphaseofsoftwarelifecycle.ó2008ElsevierLtd.Allrightsreserved.1.IntroductionJorgensenandShepperd(2007)providedasystematicreviewofsoftwaredevelopmentcostestimationstudiesandourreviewThispaperreviewsseveraljournalarticlesandconferencepa-methodologyis

7、similartotheirmethodology.Accordingtoourpersonsoftwarefaultpredictiontoevaluatetheprogressanddi-knowledge,thisisthe?rststudywhichprovidesasystematicre-rectfutureresearchonthissoftwareengineeringproblem.Manyviewofsoftwarefaultpredictionstudiesfromdifferentperspec-rese

8、archersuseddifferentapproachessuchasgeneticprogram-tives.WeposedtheeightresearchquestionsshowninTable1ming(Evett,Khoshgoftaar,Chien