Please use this identifier to cite or link to this item: http://buratest.brunel.ac.uk/handle/2438/8537
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dc.contributor.authorParejo, JA-
dc.contributor.authorHierons, RM-
dc.contributor.authorBenavides, D-
dc.contributor.authorRuiz-Cortés, A-
dc.date.accessioned2014-05-30T14:47:30Z-
dc.date.available2014-05-30T14:47:30Z-
dc.date.issued2014-
dc.identifier.citationExpert Systems with Applications, 41(8), 3975 - 3992, 2014en_US
dc.identifier.issn0957-4174-
dc.identifier.urihttp://www.sciencedirect.com/science/article/pii/S0957417413010038en
dc.identifier.urihttp://bura.brunel.ac.uk/handle/2438/8537-
dc.descriptionThis is the post-print version of the final paper published in Expert Systems with Applications. The published article is available from the link below. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. Copyright @ 2014 Elsevier B.V.en_US
dc.description.abstractA feature model is a compact representation of the products of a software product line. The automated extraction of information from feature models is a thriving topic involving numerous analysis operations, techniques and tools. Performance evaluations in this domain mainly rely on the use of random feature models. However, these only provide a rough idea of the behaviour of the tools with average problems and are not sufficient to reveal their real strengths and weaknesses. In this article, we propose to model the problem of finding computationally hard feature models as an optimization problem and we solve it using a novel evolutionary algorithm for optimized feature models (ETHOM). Given a tool and an analysis operation, ETHOM generates input models of a predefined size maximizing aspects such as the execution time or the memory consumption of the tool when performing the operation over the model. This allows users and developers to know the performance of tools in pessimistic cases providing a better idea of their real power and revealing performance bugs. Experiments using ETHOM on a number of analyses and tools have successfully identified models producing much longer executions times and higher memory consumption than those obtained with random models of identical or even larger size.en_US
dc.description.sponsorshipEuropean Commission (FEDER), the Spanish Government and the Andalusian Government.en_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.subjectSearch-based testingen_US
dc.subjectSoftware product linesen_US
dc.subjectEvolutionary algorithmsen_US
dc.subjectFeature modelsen_US
dc.subjectPerformance testingen_US
dc.subjectAutomated analysisen_US
dc.titleAutomated generation of computationally hard feature models using evolutionary algorithmsen_US
dc.typeArticleen_US
dc.identifier.doihttp://dx.doi.org/10.1016/j.eswa.2013.12.028-
pubs.organisational-data/Brunel-
pubs.organisational-data/Brunel/Brunel Active Staff-
pubs.organisational-data/Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths-
pubs.organisational-data/Brunel/Brunel Active Staff/School of Info. Systems, Comp & Maths/IS and Computing-
pubs.organisational-data/Brunel/University Research Centres and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups-
pubs.organisational-data/Brunel/University Research Centres and Groups/School of Information Systems, Computing and Mathematics - URCs and Groups/Centre for Information and Knowledge Management-
Appears in Collections:Publications
Computer Science
Dept of Computer Science Research Papers

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