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Variance of ML-based software fault predictors: are we really improving fault prediction?

dc.contributor.authorShahini, Xhulja
dc.contributor.authorBubel, Domenic
dc.contributor.authorMetzger, Andreas
dc.contributor.editorRabiser, Rick
dc.contributor.editorWimmer, Manuel
dc.contributor.editorGroher, Iris
dc.contributor.editorWortmann, Andreas
dc.contributor.editorWiesmayr, Bianca
dc.date.accessioned2024-02-19T09:22:48Z
dc.date.available2024-02-19T09:22:48Z
dc.date.issued2024
dc.identifier.doi10.18420/sw2024_26
dc.identifier.isbn978-3-88579-737-1
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/43569
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftware Engineering 2024 (SE 2024)
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-343
dc.subjectMachine learning
dc.subjectFault prediction
dc.subjectQuality assurance
dc.subjectVariance analysis
dc.subjectNon-determinism
dc.titleVariance of ML-based software fault predictors: are we really improving fault prediction?en
dc.typeText/Conference Paper
gi.citation.endPage90
gi.citation.publisherPlaceBonn
gi.citation.startPage89
gi.conference.date26. Februar-1. März 2024
gi.conference.locationLinz, Österreich
gi.conference.sessiontitleArtificial Intelligence

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