How We Refactor and How We Document it? On the Use of Supervised Machine Learning Algorithms to Classify Refactoring Documentation

• URL: http://arxiv.org/abs/2010.13890v1
• Date: Mon, 26 Oct 2020 20:33:17 GMT
• Title: How We Refactor and How We Document it? On the Use of Supervised Machine Learning Algorithms to Classify Refactoring Documentation
• Authors: Eman Abdullah AlOmar, Anthony Peruma, Mohamed Wiem Mkaouer, Christian Newman, Ali Ouni, Marouane Kessentini
• Abstract summary: Refactoring is the art of improving the design of a system without altering its external behavior. This study categorizes commits into 3 categories, namely, Internal QA, External QA, and Code Smell Resolution, along with the traditional BugFix and Functional categories. To better understand our classification results, we analyzed commit messages to extract patterns that developers regularly use to describe their smells.
• Score: 25.626914797750487
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Refactoring is the art of improving the design of a system without altering its external behavior. Refactoring has become a well established and disciplined software engineering practice that has attracted a significant amount of research presuming that refactoring is primarily motivated by the need to improve system structures. However, recent studies have shown that developers may incorporate refactorings in other development activities that go beyond improving the design. Unfortunately, these studies are limited to developer interviews and a reduced set of projects. To cope with the above-mentioned limitations, we aim to better understand what motivates developers to apply refactoring by mining and classifying a large set of 111,884 commits containing refactorings, extracted from 800 Java projects. We trained a multi-class classifier to categorize these commits into 3 categories, namely, Internal QA, External QA, and Code Smell Resolution, along with the traditional BugFix and Functional categories. This classification challenges the original definition of refactoring, being exclusive to improving the design and fixing code smells. Further, to better understand our classification results, we analyzed commit messages to extract textual patterns that developers regularly use to describe their refactorings. The results show that (1) fixing code smells is not the main driver for developers to refactoring their codebases. Refactoring is solicited for a wide variety of reasons, going beyond its traditional definition; (2) the distribution of refactorings differs between production and test files; (3) developers use several patterns to purposefully target refactoring; (4) the textual patterns, extracted from commit messages, provide better coverage for how developers document their refactorings.

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