Fuzzy set and cache-based approach for bug triaging

Ahmed Tamrawi, T. Nguyen, Jafar M. Al-Kofahi, T. Nguyen
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引用次数: 162

Abstract

Bug triaging aims to assign a bug to the most appropriate fixer. That task is crucial in reducing time and efforts in a bug fixing process. In this paper, we propose Bugzie, a novel approach for automatic bug triaging based on fuzzy set and cache-based modeling of the bug-fixing expertise of developers. Bugzie considers a software system to have multiple technical aspects, each of which is associated with technical terms. For each technical term, it uses a fuzzy set to represent the developers who are capable/competent of fixing the bugs relevant to the corresponding aspect. The fixing correlation of a developer toward a technical term is represented by his/her membership score toward the corresponding fuzzy set. The score is calculated based on the bug reports that (s)he has fixed, and is updated as the newly fixed bug reports are available. For a new bug report, Bugzie combines the fuzzy sets corresponding to its terms and ranks the developers based on their membership scores toward that combined fuzzy set to find the most capable fixers. Our empirical results show that Bugzie achieves significantly higher accuracy and time efficiency than existing state-of-the-art approaches.
基于模糊集和缓存的bug分类方法
Bug分类的目的是将Bug分配给最合适的修复者。这项任务对于减少bug修复过程中的时间和精力至关重要。在本文中,我们提出了Bugzie,一种基于模糊集和基于缓存的开发人员错误修复专业知识建模的自动错误分类方法。Bugzie认为软件系统具有多个技术方面,每个技术方面都与技术术语相关联。对于每个技术术语,它使用一个模糊集来表示有能力/有能力修复与相应方面相关的错误的开发人员。开发人员对技术术语的固定相关性表示为他/她对相应模糊集的隶属度得分。分数是根据他修复的错误报告计算的,并在新修复的错误报告可用时更新。对于一个新的bug报告,Bugzie结合了与它的术语相对应的模糊集,并根据开发人员在这个组合模糊集上的成员得分对他们进行排名,以找到最有能力的修复者。我们的实证结果表明,与现有的最先进的方法相比,Bugzie实现了更高的准确性和时间效率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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