.png){kind=icon}
返回
Automatic Software Refactoring via Weighted Clustering in Method-Level Networks
.png){kind=icon}
PRE
AI
DOI：10.1109/TSE.2017.2679752.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
In this study, we describe a system-level multiple refactoring algorithm, which can identify the move method, move field, and extract class refactoring opportunities automatically according to the principle of high cohesion and low coupling. The algorithm works by merging and splitting related classes to obtain the optimal functionality distribution from the system-level. Furthermore, we present a weighted clustering algorithm for regrouping the entities in a system based on merged method-level networks. Using a series of preprocessing steps and preconditions, the bad smells introduced by cohesion and coupling problems can be removed from both the non-inheritance and inheritance hierarchies without changing the code behaviors. We rank the refactoring suggestions based on the anticipated benefits that they bring to the system. Based on comparisons with related research and assessing the refactoring results using quality metrics and empirical evaluation, we show that the proposed approach performs well in different systems and is beneficial from the perspective of the original developers. Finally, an open source tool is implemented to support the proposed approach.
Keyword:
Clustering analysis
cohesion
coupling
complex network
software refactoring
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
5.6
论文数:
2.9K
被引数:
1.1W
机构
引用论文
Introducing a novel mushroom from mycophagy community with emphasis on biomedical potency.png)
PLOS ONE
OAAI
PLOS ONEIF0
OAAI分享收藏Exploring the relationships between design measures and software quality in object-oriented systems
PREAI
分享收藏Evaluation of the risk factors for febrile neutropenia associated with hematological malignancy
PREAI
分享收藏
查看更多内容