dc.creator | Segura Rueda, Sergio | es |
dc.creator | Parejo Maestre, José Antonio | es |
dc.creator | Hierons, Robert M. | es |
dc.creator | Benavides Cuevas, David Felipe | es |
dc.creator | Ruiz Cortés, Antonio | |
dc.date.accessioned | 2015-04-17T11:02:38Z | |
dc.date.available | 2015-04-17T11:02:38Z | |
dc.date.issued | 2014 | es |
dc.identifier.citation | Segura 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.issn | 0957-4174 | es |
dc.identifier.uri | http://hdl.handle.net/11441/24585 | |
dc.description.abstract | A 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.sponsorship | CICYT TIN2009-07366 | |
dc.description.sponsorship | CICYT TIN2012-32273 | |
dc.description.sponsorship | Junta de Andalucía TIC-5906 | |
dc.description.sponsorship | Junta de Andalucía P12-TIC-1867 | |
dc.format | application/pdf | es |
dc.language.iso | eng | es |
dc.publisher | Elsevier | es |
dc.relation.ispartof | Expert Systems with Applications, 41 (8), 3975-3992. | |
dc.rights | Atribución-NoComercial-SinDerivadas 4.0 España | es |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0 | es |
dc.subject | Search-based testing | eng |
dc.subject | Software product lines | eng |
dc.subject | Evolutionary algorithms | eng |
dc.subject | Feature models | eng |
dc.subject | Performance testing | eng |
dc.subject | Automated analysis | eng |
dc.title | Automated Generation of Computationally Hard Feature Models Using Evolutionary Algorithms | es |
dc.type | info:eu-repo/semantics/article | es |
dc.type.version | info:eu-repo/semantics/submittedVersion | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es |
dc.contributor.affiliation | Universidad de Sevilla. Departamento de Lenguajes y Sistemas Informáticos | es |
dc.relation.projectID | TIN2009-07366 | |
dc.relation.projectID | TIN2012-32273 | |
dc.relation.projectID | TIC-5906 | |
dc.relation.projectID | P12-TIC-1867 | |
dc.relation.publisherversion | http://dx.doi.org/10.1016/j.eswa.2013.12.028 | |
dc.identifier.doi | 10.1016/j.eswa.2013.12.028 | |
dc.contributor.group | Universidad de Sevilla. TIC205: Ingeniería del Software Aplicada | |
dc.journaltitle | Expert Systems with Applications | es |
dc.publication.volumen | 41 | es |
dc.publication.issue | 8 | es |
dc.publication.initialPage | 3975 | es |
dc.publication.endPage | 3992 | es |
dc.identifier.idus | https://idus.us.es/xmlui/handle/11441/24585 | |