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dc.contributor.authorHall, Tracy
dc.contributor.authorBeecham, Sarah
dc.contributor.authorBowes, David
dc.contributor.authorGray, David
dc.contributor.authorCounsell, Steve
dc.date.accessioned2011-11-14T10:01:08Z
dc.date.available2011-11-14T10:01:08Z
dc.date.issued2011
dc.identifier.citationHall , T , Beecham , S , Bowes , D , Gray , D & Counsell , S 2011 , ' Developing Fault-Prediction Models : What the research can show industry ' , IEEE Software , vol. 28 , no. 6 , pp. 96-99 . https://doi.org/10.1109/MS.2011.138
dc.identifier.issn0740-7459
dc.identifier.otherPURE: 449349
dc.identifier.otherPURE UUID: eeffb144-9932-4231-b7b1-31dbaeba86dd
dc.identifier.otherWOS: 000296102300020
dc.identifier.otherScopus: 80054888111
dc.identifier.urihttp://hdl.handle.net/2299/6982
dc.description.abstractA systematic review of the research literature on fault-prediction models from 2000 through 2010 identified 36 studies that sufficiently defined their models and development context and methodology. The authors quantitatively analyzed 19 of these studies and the 206 models they presented. They identified several key features to help industry software developers build or optimize fault-prediction models suitable to their specific contexts.en
dc.format.extent4
dc.language.isoeng
dc.relation.ispartofIEEE Software
dc.subjectfault-prediction models
dc.titleDeveloping Fault-Prediction Models : What the research can show industryen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.description.statusPeer reviewed
dc.identifier.urlhttp://www.scopus.com/inward/record.url?scp=80054888111&partnerID=8YFLogxK
rioxxterms.versionVoR
rioxxterms.versionofrecordhttps://doi.org/10.1109/MS.2011.138
rioxxterms.typeJournal Article/Review
herts.preservation.rarelyaccessedtrue


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