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A Large-Scale Longitudinal Study of Flaky Tests

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dc.contributor.authorLam, Wing
dc.contributor.authorWinter, Stefan
dc.contributor.authorWei, Anjiang
dc.contributor.authorXie, Tao
dc.contributor.authorMarinov, Darko
dc.contributor.authorBell, Jonathan
dc.contributor.editorGrunske, Lars
dc.contributor.editorSiegmund, Janet
dc.contributor.editorVogelsang, Andreas
dc.date.accessioned2022-01-19T12:56:53Z
dc.date.available2022-01-19T12:56:53Z
dc.date.issued2022
dc.description.abstractFlaky tests that non-deterministically pass or fail without any code changes constitute an impediment to regression testing. To understand when and how flaky tests can be detected most efficiently, we analyzed the commit histories of known flaky tests. We find that 75% of flaky tests are flaky when added, indicating substantial value for developers to run detectors specifically on newly added tests. The percentage of flaky tests that can be detected early increases to 85% when detectors are run on both newly added and directly modified tests.en
dc.identifier.doi10.18420/se2022-ws-018
dc.identifier.isbn978-3-88579-714-2
dc.identifier.pissn1617-5468
dc.identifier.urihttps://dl.gi.de/handle/20.500.12116/37970
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftware Engineering 2022
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-320
dc.subjectFlaky Tests
dc.subjectRegression Testing
dc.titleA Large-Scale Longitudinal Study of Flaky Testsen
dc.typeText/Conference Paper
gi.citation.endPage59
gi.citation.publisherPlaceBonn
gi.citation.startPage57
gi.conference.date21.-25. Feburar 2022
gi.conference.locationBerlin/Virtuell
gi.conference.sessiontitleWissenschaftliches Hauptprogramm

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P320 - Software Engineering 2022