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Guiding random test generation with program analysis

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dc.contributor.authorMa, Lei
dc.contributor.authorArtho, Cyrille Valentin
dc.contributor.authorZhang, Cheng
dc.contributor.authorSato, Hiroyuki
dc.contributor.authorGmeiner, Johannes
dc.contributor.authorRamler, Rudolf
dc.contributor.editorKnoop, Jens
dc.contributor.editorZdun, Uwe
dc.date.accessioned2017-06-21T07:37:15Z
dc.date.available2017-06-21T07:37:15Z
dc.date.issued2016
dc.description.abstractRandom test generation is effective in creating method sequences for exercising the software under test. However, black-box approaches for random testing are known to suffer from low code coverage and limited defect detection ability. Analyzing the software under test and using the extracted knowledge to guide test generation can help to overcome these limitations. We developed a random test case generator augmented by a combination of six static and dynamic program analysis techniques. Our tool GRT (Guided Random Testing) has been evaluated on realworld software systems as well as Defects4J benchmarks. It outperformed related approaches in terms of code coverage, mutation score and detected faults. The results show a considerable improvement potential of random test generation when combined with advanced analysis techniques.en
dc.identifier.isbn978-3-88579-646-6
dc.identifier.pissn1617-5468
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftware Engineering 2016
dc.relation.ispartofseriesLecture Notes in Informatics (LNI) - Proceedings, Volume P-252
dc.titleGuiding random test generation with program analysisen
dc.typeText/Conference Paper
gi.citation.endPage16
gi.citation.publisherPlaceBonn
gi.citation.startPage15
gi.conference.date23.-26. Februar 2016
gi.conference.locationWien

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P252 - Software Engineering 2016