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dc.contributor.authorBarbudo Lunar, Rafael
dc.contributor.authorRamírez-Quesada, Aurora
dc.contributor.authorServant-Cortés, Francisco Javier 
dc.contributor.authorRomero-Salguero, José Raúl
dc.date.accessioned2024-11-25T11:52:46Z
dc.date.available2024-11-25T11:52:46Z
dc.date.issued2021
dc.identifier.citationRafael Barbudo, Aurora Ramírez, Francisco Servant, José Raúl Romero, GEML: A grammar-based evolutionary machine learning approach for design-pattern detection, Journal of Systems and Software, V olume 175, 2021, 110919, ISSN 0164-1212, DOI: https://doi.org/10.1016/j.jss.2021.110919es_ES
dc.identifier.urihttps://hdl.handle.net/10630/35292
dc.descriptionPolítica de acceso abierto tomada de: https://openpolicyfinder.jisc.ac.uk/id/publication/14118es_ES
dc.description.abstractDesign patterns (DPs) are recognised as a good practice in software development. However, the lack of appropriate documentation often hampers traceability, and their benefits are blurred among thousands of lines of code. Automatic methods for DP detection have become relevant but are usually based on the rigid analysis of either software metrics or specific properties of the source code. We propose GEML, a novel detection approach based on evolutionary machine learning using software properties of diverse nature. Firstly, GEML makes use of an evolutionary algorithm to extract those characteristics that better describe the DP, formulated in terms of human-readable rules, whose syntax is conformant with a context-free grammar. Secondly, a rule-based classifier is built to predict whether new code contains a hidden DP implementation. GEML has been validated over five DPs taken from a public repository recurrently adopted by machine learning studies. Then, we increase this number up to 15 diverse DPs, showing its effectiveness and robustness in terms of detection capability. An initial parameter study served to tune a parameter setup whose performance guarantees the general applicability of this approach without the need to adjust complex parameters to a specific pattern. Finally, a demonstration tool is also provided.es_ES
dc.description.sponsorshipMEC TIN2017-83445-P, MEC FPU17/00799, University of Córdoba Plan propio - mod. 2.4es_ES
dc.language.isoenges_ES
dc.publisherElsevieres_ES
dc.rightsinfo:eu-repo/semantics/openAccesses_ES
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectSoftware - Diseñoes_ES
dc.subjectAprendizaje automáticoes_ES
dc.subject.otherDesign pattern detectiones_ES
dc.subject.otherReverse engineeringes_ES
dc.subject.otherMachine learninges_ES
dc.subject.otherAssociative classificationes_ES
dc.subject.otherGrammar-guided genetic programminges_ES
dc.titleGEML: A Grammar-based Evolutionary Machine Learning Approach for Design-Pattern Detection.es_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.identifier.doi10.1016/j.jss.2021.110919
dc.rights.ccAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.type.hasVersioninfo:eu-repo/semantics/submittedVersiones_ES


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