Toward Proactive Refactoring: An Exploratory Study on Decaying Modules

Abstract

Source code quality is often measured using code smell, which is an indicator of design flaw or problem in the source code. Code smells can be detected using tools such as static analyzer that detects code smells based on source code metrics. Further, developers perform refactoring activities based on the result of such detection tools to improve source code quality. However, such approach can be considered as reactive refactoring, i.e., developers react to code smells after they occur. This means that developers first suffer the effects of low quality source code (e.g., low readability and understandability) before they start solving code smells. In this study, we focus on proactive refactoring, i.e., refactoring source code before it becomes smelly. This approach would allow developers to maintain source code quality without having to suffer the impact of code smells. To support the proactive refactoring process, we propose a technique to detect decaying modules, which are non-smelly modules that are about to become smelly. We present empirical studies on open source projects with the aim of studying the characteristics of decaying modules. Additionally, to facilitate developers in the refactoring planning process, we perform a study on using a machine learning technique to predict decaying modules and report a factor that contributes most to the performance of the model under consideration.