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dc.contributor.authorTahvili, Sahar
dc.contributor.authorHatvani, Leo
dc.contributor.authorRamentol, Enislay
dc.contributor.authorPimentel, Rita
dc.contributor.authorAfzal, Wasif
dc.contributor.authorHerrera, Francisco
dc.date.accessioned2021-02-15T09:24:28Z
dc.date.available2021-02-15T09:24:28Z
dc.date.created2020-11-30T11:24:41Z
dc.date.issued2020
dc.identifier.citationEngineering Applications of Artificial Intelligence. 2020, 95 .en_US
dc.identifier.issn0952-1976
dc.identifier.urihttps://hdl.handle.net/11250/2727984
dc.description.abstractDetecting the dependency between integration test cases plays a vital role in the area of software test optimization. Classifying test cases into two main classes – dependent and independent – can be employed for several test optimization purposes such as parallel test execution, test automation, test case selection and prioritization, and test suite reduction. This task can be seen as an imbalanced classification problem due to the test cases’ distribution. Often the number of dependent and independent test cases is uneven, which is related to the testing level, testing environment and complexity of the system under test. In this study, we propose a novel methodology that consists of two main steps. Firstly, by using natural language processing we analyze the test cases’ specifications and turn them into a numeric vector. Secondly, by using the obtained data vectors, we classify each test case into a dependent or an independent class. We carry out a supervised learning approach using different methods for handling imbalanced datasets. The feasibility and possible generalization of the proposed methodology is evaluated in two industrial projects at Bombardier Transportation, Sweden, which indicates promising results.en_US
dc.language.isoengen_US
dc.publisherElsevieren_US
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.titleA novel methodology to classify test cases using natural language processing and imbalanced learningen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.pagenumber13en_US
dc.source.volume95en_US
dc.source.journalEngineering Applications of Artificial Intelligenceen_US
dc.identifier.doi10.1016/j.engappai.2020.103878
dc.identifier.cristin1854021
dc.description.localcode© 2020 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/)en_US
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1


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