GEML: A Grammar-based Evolutionary Machine Learning Approach for Design-Pattern Detection
This addresses the challenge of traceability and documentation for software developers, though it appears incremental as it builds on existing evolutionary and rule-based methods.
The authors tackled the problem of automatically detecting design patterns in software code by proposing GEML, a grammar-based evolutionary machine learning approach that extracts human-readable rules and builds a rule-based classifier, showing effectiveness and robustness in detecting up to 15 diverse design patterns.
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.