GEML: A grammar-based evolutionary machine learning approach for design-pattern detection

作     者：Barbudo, Rafael Ramirez, Aurora Servant, Francisco Romero, Jose Raul 

作者机构：Univ Cordoba Dept Comp Sci & Numer Anal Cordoba 14071 Spain Virginia Tech Dept Comp Sci Blacksburg VA 24060 USA 

出 版 物：《JOURNAL OF SYSTEMS AND SOFTWARE》 (系统与软件杂志)

年 卷 期：2021年第175卷

页      面：110919-110919页

核心收录：

学科分类：08[工学] 0835[工学-软件工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Spanish Ministry of Economy and Competitiveness [TIN2017-83445-P] Spanish Ministry of Education under the FPU program [FPU17/00799] University of Cordoba, Spain 

主　　题：Design pattern detection Reverse engineering Machine learning Associative classification Grammar-guided genetic programming 

摘      要：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. (c) 2021 Elsevier Inc. All rights reserved.