ReLink:恢复bug和更改之间的链接

Rongxin Wu, Hongyu Zhang, Sunghun Kim, S. Cheung
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引用次数: 359

摘要

软件缺陷信息,包括错误和提交的更改之间的链接,在软件维护中扮演着重要的角色,例如度量质量和预测缺陷。通常,使用启发式方法(例如在更改日志中搜索特定的关键字和错误id)自动从更改日志和错误报告中挖掘链接。然而,这些启发式的准确性取决于变更日志的质量。Bird等人发现,由于变更日志中没有bug引用,导致了很多缺失的环节。他们还发现缺失的链接会导致有偏差的缺陷信息,并影响缺陷预测的性能。我们手动检查了显式链接,这些链接在更改日志中具有显式的错误id,并观察到这些链接显示了某些特性。基于我们的观察,我们开发了一种自动链接恢复算法,ReLink,它可以自动从显式链接中学习特征标准来恢复缺失的链接。我们将ReLink应用于三个开源项目。ReLink可靠地识别链接,平均准确率为89%,召回率为78%,而传统的启发式方法的准确率为91%,召回率为64%。我们还评估了恢复链接对软件可维护性度量和缺陷预测的影响,发现ReLink的结果比传统的启发式方法产生了明显更好的准确性。
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ReLink: recovering links between bugs and changes
Software defect information, including links between bugs and committed changes, plays an important role in software maintenance such as measuring quality and predicting defects. Usually, the links are automatically mined from change logs and bug reports using heuristics such as searching for specific keywords and bug IDs in change logs. However, the accuracy of these heuristics depends on the quality of change logs. Bird et al. found that there are many missing links due to the absence of bug references in change logs. They also found that the missing links lead to biased defect information, and it affects defect prediction performance. We manually inspected the explicit links, which have explicit bug IDs in change logs and observed that the links exhibit certain features. Based on our observation, we developed an automatic link recovery algorithm, ReLink, which automatically learns criteria of features from explicit links to recover missing links. We applied ReLink to three open source projects. ReLink reliably identified links with 89% precision and 78% recall on average, while the traditional heuristics alone achieve 91% precision and 64% recall. We also evaluated the impact of recovered links on software maintainability measurement and defect prediction, and found the results of ReLink yields significantly better accuracy than those of traditional heuristics.
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