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Model-based mutation testing - Approach and case studies

dc.contributor.author Belli, F.
dc.contributor.author Budnik, C.J.
dc.contributor.author Hollmann, A.
dc.contributor.author Tuglular, T.
dc.contributor.author Wong, W.E.
dc.date.accessioned 2023-10-30T08:07:09Z
dc.date.available 2023-10-30T08:07:09Z
dc.date.issued 2016
dc.description.abstract This paper rigorously introduces the concept of model-based mutation testing (MBMT) and positions it in the landscape of mutation testing. Two elementary mutation operators, insertion and omission, are exemplarily applied to a hierarchy of graph-based models of increasing expressive power including directed graphs, event sequence graphs, finite-state machines and statecharts. Test cases generated based on the mutated models (mutants) are used to determine not only whether each mutant can be killed but also whether there are any faults in the corresponding system under consideration (SUC) developed based on the original model. Novelties of our approach are: (1) evaluation of the fault detection capability (in terms of revealing faults in the SUC) of test sets generated based on the mutated models, and (2) superseding of the great variety of existing mutation operators by iterations and combinations of the two proposed elementary operators. Three case studies were conducted on industrial and commercial real-life systems to demonstrate the feasibility of using the proposed MBMT approach in detecting faults in SUC, and to analyze its characteristic features. Our experimental data suggest that test sets generated based on the mutated models created by insertion operators are more effective in revealing faults in SUC than those generated by omission operators. Worth noting is that test sets following the MBMT approach were able to detect faults in the systems that were tested by manufacturers and independent testing organizations before they were released. © 2016 Elsevier B.V. All rights reserved. en_US
dc.identifier.citation 62
dc.identifier.doi 10.1016/j.scico.2016.01.003
dc.identifier.issn 0167-6423
dc.identifier.scopus 2-s2.0-84956598573
dc.identifier.uri https://doi.org/10.1016/j.scico.2016.01.003
dc.identifier.uri http://65.108.157.135:4000/handle/123456789/72
dc.language.iso en en_US
dc.publisher Elsevier B.V. en_US
dc.relation.ispartof Science of Computer Programming en_US
dc.rights info:eu-repo/semantics/closedAccess en_US
dc.subject Fault detection capability en_US
dc.subject Model-based mutation testing en_US
dc.subject Model-based testing en_US
dc.subject Mutation operator en_US
dc.subject Mutation testing en_US
dc.title Model-based mutation testing - Approach and case studies en_US
dc.type Article en_US
dspace.entity.type Publication
gdc.bip.impulseclass C4
gdc.bip.influenceclass C5
gdc.bip.popularityclass C4
gdc.description.department Izmir Institute of Technology İYTE en_US
gdc.description.departmenttemp Belli, F., Department of Computer Science, Electrical Engineering and Mathematics, University of Paderborn, Germany; Budnik, C.J., Siemens Corporation, Corporate Technology, United States; Hollmann, A., Andagon GmbH, Cologne, Germany; Tuglular, T., Department of Computer Engineering, Izmir Institute of Technology, Turkey; Wong, W.E., Department of Computer Science, University of Texas at Dallas, United States en_US
gdc.description.publicationcategory Makale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı en_US
gdc.description.scopusquality N/A
gdc.description.volume 120 en_US
gdc.description.wosquality N/A
gdc.oaire.accepatencedate 2016-05-01
gdc.oaire.accesstype Hybrid
gdc.oaire.diamondjournal false
gdc.oaire.downloads 117
gdc.oaire.impulse 20
gdc.oaire.influence 6.6714736E-9
gdc.oaire.influencealt 48
gdc.oaire.isgreen true
gdc.oaire.keywords Fault detection
gdc.oaire.keywords Software testing
gdc.oaire.magid 2281922300
gdc.oaire.popularity 2.9279002E-8
gdc.oaire.popularityalt 17.063654
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gdc.oaire.relevantdates created:2016-01-21
gdc.oaire.relevantdates published-print:2016-05-01
gdc.oaire.relevantdates published-online:2016-01-21
gdc.oaire.sciencefields 02020701 Software engineering/Computer occupations
gdc.oaire.sciencefields 0202 electrical engineering, electronic engineering, information engineering
gdc.oaire.sciencefields 020201 artificial intelligence & image processing
gdc.oaire.sciencefields 020207 software engineering
gdc.oaire.sciencefields 02 engineering and technology
gdc.oaire.sciencefields 02020102 Mathematical optimization/Evolutionary algorithms
gdc.oaire.views 65
gdc.opencitations.count 45
gdc.plumx.crossrefcites 17
gdc.plumx.mendeley 70
gdc.plumx.scopuscites 68
gdc.scopus.citedbycount 71
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