Please use this identifier to cite or link to this item: http://bura.brunel.ac.uk/handle/2438/13330
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dc.contributor.authorBowes, D-
dc.contributor.authorHall, T-
dc.contributor.authorHarman, M-
dc.contributor.authorJia, Y-
dc.contributor.authorSarro, F-
dc.contributor.authorWu, F-
dc.date.accessioned2016-10-11T10:59:36Z-
dc.date.accessioned2016-10-11T11:15:21Z-
dc.date.available2016-07-18-
dc.date.available2016-10-11T11:15:21Z-
dc.date.issued2016-
dc.identifier.citationISSTA 2016 - Proceedings of the 25th International Symposium on Software Testing and Analysis, 2016, pp. 330 - 341en_US
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/13330-
dc.description.abstractWe introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, applying 4 di↵erent predictive modelling techniques to 3 large real world systems (both open and closed source). The results show that our proposal can significantly (p 0.05) improve fault prediction performance. Moreover, mutation based metrics lie in the top 5% most frequently relied upon fault predictors in 10 of the 12 sets of experiments, and provide the majority of the top ten fault predictors in 9 of the 12 sets of experiments.en_US
dc.description.urihttp://www0.cs.ucl.ac.uk/staff/F.Sarro/resource/papers/ISSTA2016-Bowesetal.pdf-
dc.format.extent330 - 341-
dc.language.isoenen_US
dc.subjectSoftware Fault Predictionen_US
dc.subjectSoftware Defect Predictionen_US
dc.subjectMutation Testingen_US
dc.subjectSoftware Metricsen_US
dc.subjectEmpirical Studyen_US
dc.titleMutation-aware fault predictionen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1145/2931037.2931039-
dc.relation.isPartOfISSTA 2016 - Proceedings of the 25th International Symposium on Software Testing and Analysis-
pubs.publication-statusPublished-
Appears in Collections:Dept of Computer Science Research Papers

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