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An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing
| datacite.subject.fos | Ciências Naturais::Ciências da Computação e da Informação | |
| datacite.subject.fos | Ciências Naturais::Matemáticas | |
| dc.contributor.author | Ribeiro, José | |
| dc.contributor.author | Zenha-Rela, Mário Alberto | |
| dc.contributor.author | Vega, Francisco Fernández de | |
| dc.date.accessioned | 2025-06-23T11:54:36Z | |
| dc.date.available | 2025-06-23T11:54:36Z | |
| dc.date.issued | 2009-07 | |
| dc.description | 2009 - 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009, 8 July 2009 through 12 July 2009 - Code 78693 | |
| dc.description.abstract | 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 algorithm's efficiency considerably, while introducing a negligible computational overhead. | eng |
| dc.description.sponsorship | This work has been partially supported by project TIN2007-68083-C02 (Spanish Ministry of Education and Culture, NonHierarchical Network Evolutionary System Project – NoHNES). | |
| dc.identifier.citation | José Carlos B. Ribeiro, Mário Alberto Zenha-Rela, and Francisco Fernández de Vega. 2009. An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing. In Proceedings of the 11th Annual conference on Genetic and evolutionary computation (GECCO '09). Association for Computing Machinery, New York, NY, USA, 1949–1950. https://doi.org/10.1145/1569901.1570253. | |
| dc.identifier.doi | 10.1145/1569901.1570253 | |
| dc.identifier.isbn | 9781605583259 | |
| dc.identifier.uri | http://hdl.handle.net/10400.8/13374 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Association for Computing Machinery (ACM) | |
| dc.relation | TIN2007-68083-C02 | |
| dc.relation.hasversion | https://dl.acm.org/doi/10.1145/1569901.1570253 | |
| dc.relation.ispartof | Proceedings of the 11th Annual conference on Genetic and evolutionary computation | |
| dc.rights.uri | N/A | |
| dc.subject | Evolutionary Testing | |
| dc.subject | Search-Based Software Engineering | |
| dc.subject | Genetic Programming | |
| dc.subject | Adaptive Evolutionary Algorithms | |
| dc.title | An adaptive strategy for improving the performance of genetic programming-based approaches to evolutionary testing | eng |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.citation.conferenceDate | 2009-07 | |
| oaire.citation.conferencePlace | Montreal, Québec, Canada | |
| oaire.citation.endPage | 1950 | |
| oaire.citation.startPage | 1949 | |
| oaire.citation.title | GECCO '09: Proceedings of the 11th Annual conference on Genetic and evolutionary computation | |
| oaire.version | http://purl.org/coar/version/c_ab4af688f83e57aa | |
| person.familyName | Ribeiro | |
| person.givenName | José | |
| person.identifier | 662638 | |
| person.identifier.ciencia-id | 0C1B-5E3F-6830 | |
| person.identifier.orcid | 0000-0003-3019-1330 | |
| person.identifier.scopus-author-id | 55947747200 | |
| relation.isAuthorOfPublication | 4ad743c6-5db7-4208-be72-c182c7b0f8ef | |
| relation.isAuthorOfPublication.latestForDiscovery | 4ad743c6-5db7-4208-be72-c182c7b0f8ef |
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- 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 algorithm's efficiency considerably, while introducing a negligible computational overhead.
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