野外的可追溯性：自动补全不完整的追溯链接
Traceability in the Wild: Automatically Augmenting Incomplete Trace Links
AI总结：
本文针对提交与问题之间追溯链接缺失的问题，提出利用过程与文本特征训练分类器自动识别缺失的问题标签，在六个开源项目上取得平均96%召回率和33%精确率，并能有效扩充现有追溯链接。
摘要：
软件与系统可追溯性被广泛认为是支持许多软件开发任务的基本要素。当今的版本控制系统提供了内置功能，允许开发人员为每次提交（commit）标记一个或多个问题（issue）ID，从而为在功能请求、缺陷修复、提交、源代码和特定开发人员之间建立项目级可追溯性提供了基础构件。然而，我们对六个开源项目的分析表明，平均只有60%的提交被链接到具体问题。缺少这些基本链接，整个项目级链接集合将是不完整的，因此不可信赖。在本文中，我们解决了提交与问题之间缺失链接这一根本问题。我们的方法利用表征问题和代码变更的过程相关特征与文本相关特征的组合，训练一个分类器来识别提交消息中缺失的问题标签，从而生成缺失的链接。我们进行了一系列实验，在六个开源项目上评估我们的方法，并表明它能够有效地推荐用于标记问题的链接，平均召回率为96%，精确率为33%。在一项用于扩充现有追溯链接集合的相关任务中，该分类器在所有项目上返回的精确率均高于89%，召回率为50%。
英文摘要：
Software and systems traceability is widely accepted as an essential element for supporting many software development tasks. Today's version control systems provide inbuilt features that allow developers to tag each commit with one or more issue ID, thereby providing the building blocks from which project-wide traceability can be established between feature requests, bug fixes, commits, source code, and specific developers. However, our analysis of six open source projects showed that on average only 60% of the commits were linked to specific issues. Without these fundamental links the entire set of project-wide links will be incomplete, and therefore not trustworthy. In this paper we address the fundamental problem of missing links between commits and issues. Our approach leverages a combination of process and text-related features characterizing issues and code changes to train a classifier to identify missing issue tags in commit messages, thereby generating the missing links. We conducted a series of experiments to evaluate our approach against six open source projects and showed that it was able to effectively recommend links for tagging issues at an average of 96% recall and 33% precision. In a related task for augmenting a set of existing trace links, the classifier returned precision at levels greater than 89% in all projects and recall of 50%
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。
