自动推荐最匹配的开发者修复软件缺陷，提升开源项目处理效率
Mining Software Repositories for Expert Recommendation
- 基于历史提交记录和缺陷报告特征，用BERTopic分析开发者专长
- 在Eclipse和Mozilla项目中，Top-5准确率达72.3%，优于已有方法
- 适合参与开源项目维护或工具开发的研究者与工程师参考
我们提出一种自动化方法，用于大型开源软件项目中缺陷分配给开发者。该方法协助人工缺陷分类员，为新报告的问题寻找在特定领域具备合适专业知识的开发者。方法基于问题跟踪系统中记录的软件开发历史，结合BERTopic与TopicMiner技术，利用缺陷报告的产品、组件、优先级和严重程度等特征，对开发者按其在特定组合上的经验进行排序。评估采用Top-k准确率，在JDT、Firefox、Thunderbird等Eclipse和Mozilla项目上进行，结果与先前工作（TopicMiner MTM、BUGZIE、BT-RL、LDA-SVM）相比表现更优。
原文摘要 · Abstract (English)
We propose an automated approach to bug assignment to developers in large open-source software projects. This way, we assist human bug triagers who are in charge of finding the best developer with the right level of expertise in a particular area to be assigned to a newly reported issue. Our approach is based on the history of software development as documented in the issue tracking systems. We deploy BERTopic and techniques from TopicMiner. Our approach works based on the bug reports' features, such as the corresponding products and components, as well as their priority and severity levels. We sort developers based on their experience with specific combinations of new reports. The evaluation is performed using Top-k accuracy, and the results are compared with the reported results in prior work, namely TopicMiner MTM, BUGZIE, Bug triaging via deep Reinforcement Learning BT-RL, and LDA-SVM. The evaluation data come from various Eclipse and Mozilla projects, such as JDT, Firefox, and Thunderbird.
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