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dc.contributor.authorFinlay, J
dc.contributor.authorConnor, AM
dc.contributor.authorPears, R
dc.date.accessioned2013-02-26T04:31:44Z
dc.date.available2013-02-26T04:31:44Z
dc.date.copyright2011
dc.date.issued2013-02-26
dc.identifier.citation9th ACIS Conference on Software Engineering, Research Management and Applications , pp.39 - 45 (7)
dc.identifier.urihttp://hdl.handle.net/10292/5181
dc.description.abstractIn this paper, we describe the extraction of source code metrics from the Jazz repository and the application of data mining techniques to identify the most useful of those metrics for predicting the success or failure of an attempt to construct a working instance of the software product. We present results from a systematic study using the J48 classification method. The results indicate that only a relatively small number of the available software metrics that we considered have any significance for predicting the outcome of a build. These significant metrics are discussed and implication of the results discussed, particularly the relative difficulty of being able to predict failed build attempts.
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.rightsCopyright © 2011 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
dc.subjectData mining
dc.subjectJazz
dc.subjectSoftware metrics
dc.subjectSoftware repositories
dc.titleMining software metrics from Jazz
dc.typeConference Contribution
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1109/SERA.2011.40
aut.conference.typePaper Published in Proceedings
aut.relation.endpage45
aut.relation.pages7
aut.relation.startpage39
pubs.elements-id62980


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