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dc.contributor.authorGray, David
dc.contributor.authorBowes, D.
dc.contributor.authorDavey, N.
dc.contributor.authorSun, Yi
dc.contributor.authorChristianson, B.
dc.identifier.citationGray , D , Bowes , D , Davey , N , Sun , Y & Christianson , B 2009 , ' Using the support vector machine as a classification method for software defect prediction with static code metrics ' , Communications in Computer and Information Science , vol. 43 , pp. 223-234 .
dc.identifier.otherdspace: 2299/5864
dc.identifier.otherORCID: /0000-0002-3777-7476/work/76728389
dc.description“The original publication is available at” Copyright Springer [Full text of this article is not available in the UHRA]
dc.description.abstractThe automated detection of defective modules within software systems could lead to reduced development costs and more reliable software. In this work the static code metrics for a collection of modules contained within eleven NASA data sets are used with a Support Vector Machine classifier. A rigorous sequence of pre-processing steps were applied to the data prior to classification, including the balancing of both classes (defective or otherwise) and the removal of a large number of repeating instances. The Support Vector Machine in this experiment yields an average accuracy of 70% on previously unseen data.en
dc.relation.ispartofCommunications in Computer and Information Science
dc.titleUsing the support vector machine as a classification method for software defect prediction with static code metricsen
dc.contributor.institutionSchool of Computer Science
dc.contributor.institutionScience & Technology Research Institute
dc.contributor.institutionCentre for Computer Science and Informatics Research
dc.contributor.institutionDepartment of Computer Science
dc.contributor.institutionSchool of Physics, Engineering & Computer Science
dc.description.statusPeer reviewed
rioxxterms.typeJournal Article/Review

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