Generation of refactoring algorithms by grammatical evolution

• Published in: Empirical software engineering : an international journal 2022-09, Vol.27 (5), Article 110
• Main Authors: Mariani, Thainá, Kessentini, Marouane, Vergilio, Silvia Regina
• Format: Article
• Language: English
• Subjects: Advances in Search-Based Software Engineering (SSBSE); Algorithms; Compilers; Computer Science; Evolution; Interpreters; Machine learning; Modularity; Prediction models; Programming Languages; Quality management; Search algorithms; Software Engineering/Programming and Operating Systems
• ISSN: 1382-3256; 1573-7616
• DOI: 10.1007/s10664-022-10151-4

Recent machine learning studies present accurate results generating prediction models to identify refactoring operations for a program. However, such works are limited to prediction, i.e., they learn refactoring operations strictly as applied by developers, but there are possibilities that they might not think. On the other hand, the Search-Based Software Refactoring (SBR) field applies search algorithms to find refactoring operations in a vast space of possibilities to improve diverse quality attributes. Nevertheless, existing SBR approaches do not generate a model as machine learning studies, and then, they need to be reapplied individually for each program needing refactoring. To mitigate this limitation, this work introduces a novel SBR learning approach that generates refactoring algorithms capable of providing refactoring operations to several programs. These algorithms are composed of procedures that use rules to determine the refactoring operations. To create the algorithms, a learning process first extracts refactoring patterns from programs by grouping their elements that were refactored in similar ways. After that, a Grammatical Evolution (GE) is applied to generate the algorithms based on a grammar encompassing details of the extracted patterns. GE works to generate an algorithm that provides refactoring operations similar to those applied in practice while improving quality attributes, such as modularity. The approach is evaluated using refactoring data from 40 Java programs of GitHub repositories. The algorithms are tested against different programs, obtaining an overall average of 60% of modularity improvement and 50% of similarity with actual refactoring operations.