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Assessing single-objective performance convergence and time complexity for refactoring detection

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dc.creatorNader-Palacio D.spa
dc.creatorRodríguez-Cárdenas D.spa
dc.creatorGomez J.spa
dc.date.accessioned2020-05-26T00:08:51Z
dc.date.available2020-05-26T00:08:51Z
dc.date.created2018spa
dc.description.abstractThe automatic detection of refactoring recommendations has been tackled in prior optimization studies involving bad code smells, semantic coherence and importance of classes; however, such studies informally addressed formalisms to standardize and replicate refactoring models. We propose to assess the refactoring detection by means of performance convergence and time complexity. Since the reported approaches are diicult to reproduce, we employ an Artiicial Refactoring Generation (ARGen) as a formal and naive computational solution for the Refactoring Detection Problem. ARGen is able to detect massive refactoring's sets in feasible areas of the search space. We used a refactoring formalization to adapt search techniques (Hill Climbing, Simulated Annealing and Hybrid Adaptive Evolutionary Algorithm) that assess the performance and complexity on three open software systems. Combinatorial techniques are limited in solving the Refactoring Detection Problem due to the relevance of developers' criteria (human factor) when designing reconstructions. Without performance convergence and time complexity analysis, a software empirical analysis that utilizes search techniques is incomplete. © 2018 Copyright held by the owner/author(s). Publication rights licensed to Association for Computing Machinery.eng
dc.format.mimetypeapplication/pdf
dc.identifier.doihttps://doi.org/10.1145/3205651.3208294
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/24121
dc.language.isoengspa
dc.publisherAssociation for Computing Machinery, Incspa
dc.relation.citationEndPage1613
dc.relation.citationStartPage1606
dc.relation.citationTitleGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion
dc.relation.ispartofGECCO 2018 Companion - Proceedings of the 2018 Genetic and Evolutionary Computation Conference Companion,(2018); pp. 1606-1613spa
dc.relation.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85051547001&doi=10.1145%2f3205651.3208294&partnerID=40&md5=df4c2d7066a5f7f75ea547bc508a06c0spa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccess
dc.rights.accesoAbierto (Texto Completo)spa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordCombinatorial optimizationspa
dc.subject.keywordComputer software maintenancespa
dc.subject.keywordInformation analysisspa
dc.subject.keywordSemanticsspa
dc.subject.keywordSimulated annealingspa
dc.subject.keywordAutomatic Detectionspa
dc.subject.keywordCombinatorial techniquesspa
dc.subject.keywordComputational solutionsspa
dc.subject.keywordEmpirical analysisspa
dc.subject.keywordMathematical softwarespa
dc.subject.keywordOptimization studiesspa
dc.subject.keywordRefactoringsspa
dc.subject.keywordTime complexity analysisspa
dc.subject.keywordEvolutionary algorithmsspa
dc.subject.keywordCombinatorial Optimizationspa
dc.subject.keywordMathematical Software Performancespa
dc.subject.keywordRefactoringspa
dc.subject.keywordSoftware Maintenancespa
dc.titleAssessing single-objective performance convergence and time complexity for refactoring detectionspa
dc.typeconferenceObjecteng
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersion
dc.type.spaDocumento de conferenciaspa
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