RT Journal Article
T1 GEML: A Grammar-based Evolutionary Machine Learning Approach for Design-Pattern Detection.
A1 Barbudo Lunar, Rafael
A1 Ramírez-Quesada, Aurora
A1 Servant-Cortés, Francisco Javier
A1 Romero-Salguero, José Raúl
K1 Software - Diseño
K1 Aprendizaje automático
AB Design 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.
PB Elsevier
YR 2021
FD 2021
LK https://hdl.handle.net/10630/35292
UL https://hdl.handle.net/10630/35292
LA eng
NO Rafael 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.2021.110919
NO Política de acceso abierto tomada de: https://openpolicyfinder.jisc.ac.uk/id/publication/14118
NO MEC TIN2017-83445-P, MEC FPU17/00799, University of Córdoba Plan propio - mod. 2.4
DS RIUMA. Repositorio Institucional de la Universidad de Málaga
RD 22 ene 2026