Can Automated Impact Analysis Techniques Help Predict Decaying Modules?

Sae-Lim, Natthawute; Hayashi, Shinji; Saeki, Motoshi

doi:10.1109/icsme.2019.00088

Cited by 2 publications

(2 citation statements)

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“…* This paper is revised based on [1] and [2], which appeared in proceedings of the third International Workshop on Refactoring and proceedings of the 35th IEEE International Conference on Software Maintenance and Evolution, c 2019 IEEE.…”

Section: Introductionmentioning

confidence: 99%

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Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction

Sae-Lim

Hayashi

Saeki

2021

IEICE Trans. Inf. & Syst.

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Code smells can be detected using tools such as a static analyzer that detects code smells based on source code metrics. Developers perform refactoring activities based on the result of such detection tools to improve source code quality. However, such an 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 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.

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Section: Introductionmentioning

confidence: 99%

“…This paper is revised based on our previous studies [1], [2]. The main contributions of this study are as follows.…”

Section: Introductionmentioning

confidence: 99%