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dc.contributor.authorMayo, Michael
dc.contributor.authorSpacey, Simon
dc.coverage.spatialConference held at St Petersburg, Russiaen_NZ
dc.date.accessioned2013-07-09T04:52:11Z
dc.date.available2013-07-09T04:52:11Z
dc.date.issued2013
dc.identifier.citationMayo, M. & Spacey, S. (2013). Predicting regression test failures using genetic algorithm-selected dynamic performance analysis metrics. In G. Ruhe, and Y. Zhang (Eds.), Proceedings of 5th International Symposium, SSBSE 2013, St. Petersburg, Russia, August 24-26, 2013, LNCS 8084 (pp. 158-171). Berlin Heidelberg: Springer.en_NZ
dc.identifier.urihttps://hdl.handle.net/10289/7763
dc.description.abstractA novel framework for predicting regression test failures is proposed. The basic principle embodied in the framework is to use performance analysis tools to capture the runtime behaviour of a program as it executes each test in a regression suite. The performance information is then used to build a dynamically predictive model of test outcomes. Our framework is evaluated using a genetic algorithm for dynamic metric selection in combination with state-of-the-art machine learning classifiers. We show that if a program is modified and some tests subsequently fail, then it is possible to predict with considerable accuracy which of the remaining tests will also fail which can be used to help prioritise tests in time constrained testing environments.en_NZ
dc.format.mimetypeapplication/pdf
dc.language.isoenen_NZ
dc.publisherSpringeren_NZ
dc.relation.urihttp://www.springer.com/computer/swe/book/978-3-642-39741-7en_NZ
dc.rightsThis is the author's accepted version of a paper published by Springer in the series Lecture Notes in Computer Science (LNCS). The original publication is available at www.springerlink.com.en_NZ
dc.sourceSSBSE 2013en_NZ
dc.subjectcomputer scienceen_NZ
dc.subjectregression testingen_NZ
dc.subjecttest failure predictionen_NZ
dc.subjectprogram analysisen_NZ
dc.subjectMachine learningen_NZ
dc.subjectgenetic metric selectionen_NZ
dc.titlePredicting regression test failures using genetic algorithm-selected dynamic performance analysis metricsen_NZ
dc.typeConference Contributionen_NZ
dc.identifier.doi10.1007/978-3-642-39742-4_13en_NZ
dc.relation.isPartOfProc 5th International Symposium on Search Based Software Engineeringen_NZ
pubs.begin-page158en_NZ
pubs.elements-id23136
pubs.end-page171en_NZ
pubs.finish-date2013-08-26en_NZ
pubs.place-of-publicationGermanyen_NZ
pubs.start-date2013-08-24en_NZ
pubs.volumeLNCS 8084en_NZ


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