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

dc.contributor.author	Herbold, Steffen
dc.contributor.author	Haar, Tobias
dc.contributor.editor	Engels, Gregor
dc.contributor.editor	Hebig, Regina
dc.contributor.editor	Tichy, Matthias
dc.date.accessioned	2023-01-18T13:38:40Z
dc.date.available	2023-01-18T13:38:40Z
dc.date.issued	2023
dc.description.abstract	We 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.identifier.isbn	978-3-88579-726-5
dc.identifier.pissn	1617-5468
dc.identifier.uri	https://dl.gi.de/handle/20.500.12116/40082
dc.language.iso	en
dc.publisher	Gesellschaft für Informatik e.V.
dc.relation.ispartof	Software Engineering 2023
dc.relation.ispartofseries	Lecture Notes in Informatics (LNI) - Proceedings, Volume P-332
dc.subject	Machine learning
dc.subject	Classification
dc.subject	Software testing
dc.subject	Smoke testing
dc.subject	Combinatorial testing
dc.subject	Equivalence classes
dc.subject	Boundary-value analysis
dc.title	Smoke testing for machine learning: simple tests to discover severe bugs	en
dc.type	Text/Conference Paper
gi.citation.endPage	62
gi.citation.publisherPlace	Bonn
gi.citation.startPage	61
gi.conference.date	20.–24. Februar 2023
gi.conference.location	Paderborn
gi.conference.sessiontitle	Wissenschaftliches Hauptprogramm

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