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dc.contributor.authorBowes, David
dc.contributor.authorHall, Tracy
dc.contributor.authorpetric, Jean
dc.date.accessioned2018-08-16T00:11:46Z
dc.date.available2018-08-16T00:11:46Z
dc.date.issued2017-02-07
dc.identifier.citationBowes , D , Hall , T & petric , J 2017 , ' Software defect prediction: do different classifiers find the same defects? ' , Software Quality Journal , vol. 26 , pp. 525–552 . https://doi.org/10.1007/s11219-016-9353-3
dc.identifier.issn0963-9314
dc.identifier.otherPURE: 11268994
dc.identifier.otherPURE UUID: cbf3df08-13d6-42cf-89e1-77b2996775f0
dc.identifier.otherScopus: 85011708666
dc.identifier.urihttp://hdl.handle.net/2299/20367
dc.descriptionOpen Access: This article is distributed under the terms of the Creative Commons Attribution 4.0 International License CC BY 4.0 (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
dc.description.abstractDuring the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the predictive performance ceiling of about 80% recall. We investigate the individual defects that four classifiers predict and analyse the level of prediction uncertainty produced by these classifiers. We perform a sensitivity analysis to compare the performance of Random Forest, Naïve Bayes, RPart and SVM classifiers when predicting defects in NASA, open source and commercial datasets. The defect predictions that each classifier makes is captured in a confusion matrix and the prediction uncertainty of each classifier is compared. Despite similar predictive performance values for these four classifiers, each detects different sets of defects. Some classifiers are more consistent in predicting defects than others. Our results confirm that a unique subset of defects can be detected by specific classifiers. However, while some classifiers are consistent in the predictions they make, other classifiers vary in their predictions. Given our results, we conclude that classifier ensembles with decision-making strategies not based on majority voting are likely to perform best in defect prediction.en
dc.format.extent28
dc.language.isoeng
dc.relation.ispartofSoftware Quality Journal
dc.rightsOpen
dc.subjectsoftware defect prediction
dc.subjectprediction modelling
dc.subjectmachine learning
dc.titleSoftware defect prediction: do different classifiers find the same defects?en
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.description.statusPeer reviewed
dc.description.versiontypeFinal Published version
dcterms.dateAccepted2017-02-07
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1007/s11219-016-9353-3
rioxxterms.licenseref.urihttp://creativecommons.org/licenses/by/4.0/
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue
herts.rights.accesstypeOpen


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