IEICE TRANSACTIONS on Information
Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction
Summary :
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.
- Publication
- IEICE TRANSACTIONS on Information Vol.E104-D No.10 pp.1601-1615
- Publication Date
- 2021/10/01
- Publicized
- 2021/06/28
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.2020EDP7255
- Type of Manuscript
- PAPER
- Category
- Software Engineering
Authors
Natthawute SAE-LIM
Tokyo Institute of Technology
Shinpei HAYASHI
Tokyo Institute of Technology
Motoshi SAEKI
Nanzan University
Keyword
Latest Issue
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Natthawute SAE-LIM, Shinpei HAYASHI, Motoshi SAEKI, "Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction" in IEICE TRANSACTIONS on Information,
vol. E104-D, no. 10, pp. 1601-1615, October 2021, doi: 10.1587/transinf.2020EDP7255.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2020EDP7255/_p
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@ARTICLE{e104-d_10_1601,
author={Natthawute SAE-LIM, Shinpei HAYASHI, Motoshi SAEKI, },
journal={IEICE TRANSACTIONS on Information},
title={Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction},
year={2021},
volume={E104-D},
number={10},
pages={1601-1615},
abstract={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.},
keywords={},
doi={10.1587/transinf.2020EDP7255},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Supporting Proactive Refactoring: An Exploratory Study on Decaying Modules and Their Prediction
T2 - IEICE TRANSACTIONS on Information
SP - 1601
EP - 1615
AU - Natthawute SAE-LIM
AU - Shinpei HAYASHI
AU - Motoshi SAEKI
PY - 2021
DO - 10.1587/transinf.2020EDP7255
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E104-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2021
AB - 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.
ER -
English
