MULA: A Just-In-Time Multi-labeling System for Issue Reports
MULA: A Just-In-Time Multi-labeling System for Issue Reports
复制标题
MULA：问题报告的即时多重标签系统
DOI：
10.1109/tr.2021.3074512
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发表时间：
2022-03
影响因子：
5.9
通讯作者：
Baowen Xu
中科院分区：
文献类型：
--
作者：
Xiaoyuan Xie;Su Yuhui;Songqiang Chen;Lin Chen;Jifeng Xuan;Baowen Xu
问题跟踪系统的一个非常重要的功能是将标签分配给发布报告，例如错误，功能，增强等，以便在实践中促进各种开发活动。但是，当前的作品主要基于单标签预测，由于效率低，因此不适合刚好的多标签服务。穆拉（Mula）是一个恰当的多标签系统，它可以自动分配多个标签来发行报告。据我们所知，这是基于他们的类别的在线多标签GitHub问题的第一项工作和工具。采用了显示穆拉优越性的标签模型，以及一种评估，显示了穆拉的建议与开发人员的意见之间的一致性。
A very important function of an issue tracking system is to assign labels to issue reports, such as bug, feature, enhancement, etc., in order to categorize issues to facilitate various development activities. In practice, it is very common that an issue has multiple labels. However, current works are mainly based on single-label prediction, which are not suitable for just-in-time multi-labeling services, due to the low efficiency. Therefore, in this paper, we propose MULA, a just-in-time MUlti-LAbeling system, which learns and automatically assigns multiple labels to issue reports. We have built a dataset with 81,601 entries and 11 labels, as the first benchmark for this task, and implemented a GitHub app. To the best of our knowledge, this is the first work and tool for online multi-labeling GitHub issues based on their categories. We conduct a comprehensive empirical study, including comparisons with five commonly adopted labeling models that show the superiority of MULA, as well as an evaluation that shows high consistency between MULA’s suggestions and developers’ opinions.