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Smoke testing for machine learning: simple tests to discover severe bugs

dc.contributor.authorHerbold, Steffen
dc.contributor.authorHaar, Tobias
dc.contributor.editorEngels, Gregor
dc.contributor.editorHebig, Regina
dc.contributor.editorTichy, Matthias
dc.date.accessioned2023-01-18T13:38:40Z
dc.date.available2023-01-18T13:38:40Z
dc.date.issued2023
dc.identifier.isbn978-3-88579-726-5
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/40082
dc.description.abstractWe summarize the article Smoke testing for machine learning: simple tests to discover severe bugs [HH22], which was published in Empirical Software Engineering in 2022.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftware Engineering 2023
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-332
dc.subjectMachine learning
dc.subjectClassification
dc.subjectSoftware testing
dc.subjectSmoke testing
dc.subjectCombinatorial testing
dc.subjectEquivalence classes
dc.subjectBoundary-value analysis
dc.titleSmoke testing for machine learning: simple tests to discover severe bugsen
dc.typeText/Conference Paper
dc.pubPlaceBonn
mci.reference.pages61-62
mci.conference.sessiontitleWissenschaftliches Hauptprogramm
mci.conference.locationPaderborn
mci.conference.date20.–24. Februar 2023


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