利用文本和非文本数据自动恢复问题提交链接
Automated Recovery of Issue-Commit Links Leveraging Both Textual and Non-textual Data
摘要：
一个issue记录了关于问题跟踪系统中所需更改的讨论，而一个commit则包含了版本控制系统中的更改本身。恢复问题和提交之间的链接可以促进许多软件演化任务，如错误定位和软件文档。之前对GitHub上超过50万个问题的研究报告显示，只有大约42.2%的问题是由开发人员手动链接到相关提交的。自动化提交-问题对的链接可以有助于改进所述任务。到目前为止，目前最先进的自动提交-问题链接方法精度较低，导致不可靠的结果，有时甚至会对预测的链接进行人工监督。当提交或问题中缺少文本信息时，低性能变得更加严重。目前的方法也被证明计算昂贵。我们提出了混合链接器，以克服这种限制，利用两个信息渠道;（1）一个非文本为基础的组件，操作非文本，自动记录的提交-问题对的信息来预测链接，（2）一个基于文本的一个使用文本信息的提交-问题对做同样的事情。然后，结合两个分类器的结果，Hybrid-Linker做出最终的预测。因此，每当一个组件在预测链接时出现不足时，另一个组件就会填补空白并改进结果。我们在12个项目的数据集上评估了混合链接器与竞争方法，即FRLink和DeepLink。基于查全率、查准率和F-测度，Hybrid-Linker分别达到90.1%、87.8%和88.9%。它在F指标方面也比FRLink和DeepLink高出31.3%和41.3%。此外，Hybrid-Linker在性能方面也表现出广泛的改进。
英文摘要：
An issue documents discussions around required changes in issue-tracking systems, while a commit contains the change itself in the version control systems. Recovering links between issues and commits can facilitate many software evolution tasks such as bug localization, and software documentation. A previous study on over half a million issues from GitHub reports only about 42.2% of issues are manually linked by developers to their pertinent commits. Automating the linking of commit-issue pairs can contribute to the improvement of the said tasks. By far, current state-of-the-art approaches for automated commit-issue linking suffer from low precision, leading to unreliable results, sometimes to the point that imposes human supervision on the predicted links. The low performance gets even more severe when there is a lack of textual information in either commits or issues. Current approaches are also proven computationally expensive. We propose Hybrid-Linker to overcome such limitations by exploiting two information channels; (1) a non-textual-based component that operates on non-textual, automatically recorded information of the commit-issue pairs to predict a link, and (2) a textual-based one which does the same using textual information of the commit-issue pairs. Then, combining the results from the two classifiers, Hybrid-Linker makes the final prediction. Thus, every time one component falls short in predicting a link, the other component fills the gap and improves the results. We evaluate Hybrid-Linker against competing approaches, namely FRLink and DeepLink on a dataset of 12 projects. Hybrid-Linker achieves 90.1%, 87.8%, and 88.9% based on recall, precision, and F-measure, respectively. It also outperforms FRLink and DeepLink by 31.3%, and 41.3%, regarding the F-measure. Moreover, Hybrid-Linker exhibits extensive improvements in terms of performance as well.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
