Mostrar el registro sencillo del ítem

Artículo

dc.creatorSegura Rueda, Sergioes
dc.creatorParejo Maestre, José Antonioes
dc.creatorHierons, Robert M.es
dc.creatorBenavides Cuevas, David Felipees
dc.creatorRuiz Cortés, Antonio
dc.date.accessioned2015-04-17T11:02:38Z
dc.date.available2015-04-17T11:02:38Z
dc.date.issued2014es
dc.identifier.citationSegura Rueda, S., Parejo Maestre, J.A., Hierons, R.M., Benavides Cuevas, D.F. y Ruiz Cortés, A. (2014). Automated Generation of Computationally Hard Feature Models Using Evolutionary Algorithms. Expert Systems with Applications, 41 (8), 3975-3992.
dc.identifier.issn0957-4174es
dc.identifier.urihttp://hdl.handle.net/11441/24585
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.eng
dc.description.sponsorshipCICYT TIN2009-07366
dc.description.sponsorshipCICYT TIN2012-32273
dc.description.sponsorshipJunta de Andalucía TIC-5906
dc.description.sponsorshipJunta de Andalucía P12-TIC-1867
dc.formatapplication/pdfes
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofExpert Systems with Applications, 41 (8), 3975-3992.
dc.rightsAtribución-NoComercial-SinDerivadas 4.0 Españaes
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0es
dc.subjectSearch-based testingeng
dc.subjectSoftware product lineseng
dc.subjectEvolutionary algorithmseng
dc.subjectFeature modelseng
dc.subjectPerformance testingeng
dc.subjectAutomated analysiseng
dc.titleAutomated Generation of Computationally Hard Feature Models Using Evolutionary Algorithmses
dc.typeinfo:eu-repo/semantics/articlees
dc.type.versioninfo:eu-repo/semantics/submittedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticoses
dc.relation.projectIDTIN2009-07366
dc.relation.projectIDTIN2012-32273
dc.relation.projectIDTIC-5906
dc.relation.projectIDP12-TIC-1867
dc.relation.publisherversionhttp://dx.doi.org/10.1016/j.eswa.2013.12.028
dc.identifier.doi10.1016/j.eswa.2013.12.028
dc.contributor.groupUniversidad de Sevilla. TIC205: Ingeniería del Software Aplicada
dc.journaltitleExpert Systems with Applicationses
dc.publication.volumen41es
dc.publication.issue8es
dc.publication.initialPage3975es
dc.publication.endPage3992es
dc.identifier.idushttps://idus.us.es/xmlui/handle/11441/24585

FicherosTamañoFormatoVerDescripción
file_1.pdf412.5KbIcon   [PDF] Ver/Abrir  

Este registro aparece en las siguientes colecciones

Mostrar el registro sencillo del ítem

Atribución-NoComercial-SinDerivadas 4.0 España
Excepto si se señala otra cosa, la licencia del ítem se describe como: Atribución-NoComercial-SinDerivadas 4.0 España