从外表判断提交:将提交信息熵与Travis-CI上的构建状态相关联

E. Santos, Abram Hindle
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引用次数: 32

摘要

开发人员在提交消息中总结他们对代码的更改。然而,当一条消息看起来“不寻常”时,就会对提交中包含的代码的质量产生怀疑。我们训练了n-gram语言模型,并使用交叉熵作为来自开源项目的超过120,000个提交的提交消息“不寻常”的指示器。从Travis-CI收集的构建状态被用作代码质量的代理。然后,我们比较了失败和成功提交的分布,比较了提交消息的“不寻常性”。当交叉熵与构建状态相关联时,我们的分析产生了显著的结果。
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Judging a Commit by Its Cover: Correlating Commit Message Entropy with Build Status on Travis-CI
Developers summarize their changes to code in commit messages.When a message seems “unusual’', however, this puts doubt into the quality of the code contained in the commit. We trained n-gram language models and used cross-entropy as an indicator of commit message “unusualness” of over 120,000 commits from open source projects.Build statuses collected from Travis-CI were used as a proxy for code quality. We then compared the distributions of failed and successful commits with regards to the “unusualness'’ of their commit message. Our analysis yielded significant results when correlating cross-entropy with build status.
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