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.

设计模式是软件开发中被认可的良好实践，但是缺乏适当的文档常常使得追溯困难，其益处在庞杂的代码中变得模糊。我们提出了一种基于进化机器学习和多样性软件属性的新颖检测方法GEML。通过进化算法提取出最能描述设计模式的特征，并建立基于规则的分类器来预测新代码中是否包含隐藏的设计模式实现。GEML经过验证可以有效、稳健地检测多样性的设计模式。此外，还提供了演示工具。

GEML：基于语法的进化机器学习方法用于设计模式检测