Bug‐assignment (BA), the task of ranking developers in terms of the relevance of their expertise to fix a new bug report is time consuming, which is why substantial attention has been paid to developing methods for automating it. In this article, we describe a new BA approach that relies on two key intuitions. Similar to traditional BA methods, our method constructs the expertise profile of project developers, based on the textual elements of the bugs they have fixed in the past; unlike traditional methods, however, our method considers only the programming keywords in these bug descriptions, relying on Stack Overflow as the vocabulary for these keywords. The second key intuition of our method is that recent expertise is more relevant than past expertise, which is why our method weighs the relevance of a developer's expertise based on how recently they have fixed a bug with keywords similar to the bug at hand. We evaluated our BA method using a dataset of 93k bug‐report assignments from 13 popular GitHub projects. In spite of its simplicity, our method predicts the assignee with high accuracy, outperforming state‐of‐the‐art methods.

中文翻译：

---

基于词汇和时间的错误分配：开源项目的推荐系统

错误分配 (BA)，根据他们的专业知识与修复新错误报告的相关性对开发人员进行排名的任务非常耗时，这就是为什么人们对开发自动化方法给予了大量关注。在本文中，我们描述了一种依赖于两个关键直觉的新 BA 方法。与传统的 BA 方法类似，我们的方法基于他们过去修复的错误的文本元素构建项目开发人员的专业知识档案；然而，与传统方法不同的是，我们的方法仅考虑这些错误描述中的编程关键字，依赖 Stack Overflow 作为这些关键字的词汇表。我们方法的第二个关键直觉是最近的专业知识比过去的专业知识更相关，这就是为什么我们的方法会权衡开发人员的相关性 他们的专业知识基于他们最近使用与手头错误相似的关键字修复错误的时间。我们使用来自 13 个流行 GitHub 项目的 93k 错误报告分配的数据集评估了我们的 BA 方法。尽管它很简单，但我们的方法以高精度预测受让人，优于最先进的方法。