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Adaptive Evolutionary Testing: An Adaptive Approach to Search-Based Test Case Generation for Object-Oriented Software

2010Book part Open access
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datacite.subject.fosCiências Naturais::Ciências da Computação e da Informação
datacite.subject.sdg08:Trabalho Digno e Crescimento Económico
datacite.subject.sdg09:Indústria, Inovação e Infraestruturas
datacite.subject.sdg10:Reduzir as Desigualdades
dc.contributor.authorRibeiro, José Carlos Bregieiro
dc.contributor.authorZenha-Rela, Mário Alberto
dc.contributor.authorde Vega, Francisco Fernández
dc.date.accessioned2025-11-13T11:26:44Z
dc.date.available2025-11-13T11:26:44Z
dc.date.issued2010
dc.descriptionEISBN - 9783642125386
dc.descriptionFonte: https://www.researchgate.net/publication/220948296_Adaptive_Evolutionary_Testing_An_Adaptive_Approach_to_Search-Based_Test_Case_Generation_for_Object-Oriented_Software/citation/download
dc.description.abstractAdaptive Evolutionary Algorithms are distinguished by their dynamic manipulation of selected parameters during the course of evolving a problem solution; they have an advantage over their static counterparts in that they are more reactive to the unanticipated particulars of the problem. This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an Adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the test case generation algorithm's efficiency considerably, while introducing a negligible computational overhead.eng
dc.identifier.citationRibeiro, J.C.B., Zenha-Rela, M.A., de Vega, F.F. (2010). Adaptive Evolutionary Testing: An Adaptive Approach to Search-Based Test Case Generation for Object-Oriented Software. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds) Nature Inspired Cooperative Strategies for Optimization (NICSO 2010). Studies in Computational Intelligence, vol 284. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-12538-6_16.
dc.identifier.doi10.1007/978-3-642-12538-6_16
dc.identifier.eissn1860-9503
dc.identifier.isbn9783642125379
dc.identifier.isbn9783642125386
dc.identifier.issn1860-949X
dc.identifier.urihttp://hdl.handle.net/10400.8/14603
dc.language.isoeng
dc.peerreviewedyes
dc.publisherSpringer Nature
dc.relation.hasversionhttps://link.springer.com/chapter/10.1007/978-3-642-12538-6_16
dc.relation.ispartofStudies in Computational Intelligence
dc.relation.ispartofNature Inspired Cooperative Strategies for Optimization (NICSO 2010)
dc.rights.uriN/A
dc.subjectTest Case Generation
dc.subjectTest Data Generation
dc.subjectTest Cluster
dc.subjectConcolic Testing
dc.subjectRuntime Exception
dc.titleAdaptive Evolutionary Testing: An Adaptive Approach to Search-Based Test Case Generation for Object-Oriented Softwareeng
dc.typebook part
dspace.entity.typePublication
oaire.citation.endPage13
oaire.citation.startPage1
oaire.citation.titleStudies in Computational Intelligence
oaire.versionhttp://purl.org/coar/version/c_ab4af688f83e57aa
person.familyNameRibeiro
person.givenNameJosé
person.identifier662638
person.identifier.ciencia-id0C1B-5E3F-6830
person.identifier.orcid0000-0003-3019-1330
person.identifier.scopus-author-id55947747200
relation.isAuthorOfPublication4ad743c6-5db7-4208-be72-c182c7b0f8ef
relation.isAuthorOfPublication.latestForDiscovery4ad743c6-5db7-4208-be72-c182c7b0f8ef

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Adaptive Evolutionary Algorithms are distinguished by their dynamic manipulation of selected parameters during the course of evolving a problem solution; they have an advantage over their static counterparts in that they are more reactive to the unanticipated particulars of the problem. This paper proposes an adaptive strategy for enhancing Genetic Programming-based approaches to automatic test case generation. The main contribution of this study is that of proposing an Adaptive Evolutionary Testing methodology for promoting the introduction of relevant instructions into the generated test cases by means of mutation; the instructions from which the algorithm can choose are ranked, with their rankings being updated every generation in accordance to the feedback obtained from the individuals evaluated in the preceding generation. The experimental studies developed show that the adaptive strategy proposed improves the test case generation algorithm's efficiency considerably, while introducing a negligible computational overhead.
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