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Fault detection and prediction in an open-source software project

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dc.contributor.author English, Michael
dc.contributor.author Exton, Chris
dc.contributor.author Cleary, Brendan
dc.contributor.author Rigon, Irene
dc.date.accessioned 2012-02-02T13:30:40Z
dc.date.available 2012-02-02T13:30:40Z
dc.date.issued 2009
dc.identifier.uri http://hdl.handle.net/10344/1893
dc.description non-peer-reviewed en_US
dc.description.abstract Software maintenance continues to be a time and resource intensive activity. Any efforts that help to address the maintenance bottleneck within the software lifecycle are welcome. One area where such e orts are useful is in the identification of the parts of the source-code of a software system that are most likely to contain faults and thus require changes. We have carried out an empirical study where we have merged information from the CVS repository and the Bugzilla database for an open-source software project to investigate whether or not parts of the source-code are faulty, the number and severity of faults and the number and types of changes associated with parts of the system. We present an analysis of this information, showing that Pareto's Law holds and we evaluate the usefulness of the Chidamber and Kemerer metrics for identifying the fault-prone classes in the system analysed. en_US
dc.language.iso eng en_US
dc.publisher Association for Computing Machinery en_US
dc.relation.ispartofseries Proceedings of the 5th International Conference on Predictor Models in Software Engineering;05/2009
dc.rights "© ACM, 2009. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in PROMISE '09 Proceedings of the 5th International Conference on Predictor Models in Software Engineering http://dx.doi.org/10.1145/1540438.1540462 en_US
dc.subject empirical study en_US
dc.subject metrics en_US
dc.subject fault prediction en_US
dc.subject open source en_US
dc.title Fault detection and prediction in an open-source software project en_US
dc.type Conference item en_US
dc.type.supercollection all_ul_research en_US
dc.type.restriction none en
dc.contributor.sponsor SFI
dc.internal.authorcontactother Michael.english@ul.ie
dc.internal.authorcontactother chris.exton@ul.ie


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