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dc.contributor.authorPereira Borges Jr., Nataniel
dc.contributor.authorHavrikov, Nikolas
dc.contributor.authorZeller, Andreas
dc.contributor.editorKoziolek, Anne
dc.contributor.editorSchaefer, Ina
dc.contributor.editorSeidl, Christoph
dc.date.accessioned2020-12-17T11:57:56Z
dc.date.available2020-12-17T11:57:56Z
dc.date.issued2021
dc.identifier.isbn978-3-88579-704-3
dc.identifier.issn1617-5468
dc.identifier.urihttp://dl.gi.de/handle/20.500.12116/34527
dc.description.abstractTo systematically test a program, one needs good inputs—inputs that are valid such that they are not rejected by the program, and inputs that cover as much of the input space as possible in order to reach a maximum of functionality. We present recent techniques to systematically cover input structure. Our k-path algorithm for grammar production [HZ19] systematically covers syntactic elements of the input as well as their combinations. We show how to learn such input structures from graphical user interfaces, notably their interaction language [DBZ19]. Finally, we demonstrate that knowledge bases such as DBPedia can be a reliable source of semantically coherent inputs [Wa20]. All these techniques result in a significantly higher code coverage than state of the art.en
dc.language.isoen
dc.publisherGesellschaft für Informatik e.V.
dc.relation.ispartofSoftware Engineering 2021
dc.relation.ispartofseriesecture Notes in Informatics (LNI) - Proceedings, Volume P-310
dc.subjectgrammar
dc.subjectcoverage
dc.subjectautomated testing
dc.subjectinput generation
dc.subjectknowledge-base
dc.subjectandroid
dc.titleGenerating Tests that Cover Input Structureen
dc.typeText/ConferencePaper
dc.pubPlaceBonn
mci.reference.pages85-86
mci.conference.locationBraunschweig/Virtuell
mci.conference.date22.-26. Februar 2021
dc.identifier.doi10.18420/SE2021_31


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