c-JRefRec:移动方法重构机会的基于变化的识别

Naoya Ujihara, Ali Ouni, T. Ishio, Katsuro Inoue
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引用次数: 13

摘要

在本文中,我们提出了一个轻量级的重构推荐工具,即c-JRefRec,它基于静态和语义程序分析的四种启发式方法来识别Move Method重构机会。我们的工具旨在识别重构的机会之前,代码更改提交到代码库基于当前的代码更改,无论何时开发人员保存/编译他的代码。我们评估了我们的方法在检测Feature Envy气味方面的效率,并在三个Java开源系统和30个代码更改中推荐Move Method重构来修复它们。结果表明,我们的方法达到了0.48和0.73的召回平均精度,优于最先进的方法,即JDeodorant。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
c-JRefRec: Change-based identification of Move Method refactoring opportunities
We propose, in this paper, a lightweight refactoring recommendation tool, namely c-JRefRec, to identify Move Method refactoring opportunities based on four heuristics using static and semantic program analysis. Our tool aims at identiying refactoring opportunities before a code change is committed to the codebase based on current code changes whenever the developer saves/compiles his code. We evaluate the efficiency of our approach in detecting Feature Envy smells and recommending Move Method refactorings to fix them on three Java open-source systems and 30 code changes. Results show that our approach achieves an average precision of 0.48 and 0.73 of recall and outperforms a state-of-the-art approach namely JDeodorant.
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