论文标题
自动拉请求标题生成
Automatic Pull Request Title Generation
论文作者
论文摘要
拉请求（PRS）是现代协作编码平台（例如GitHub）的一种机制。 PRS允许开发人员告诉他人，他们的代码更改可用于合并存储库中的另一个分支。在将更改合并到分支机构之前，需要审查和批准存储库的核心团队。通常，审阅者需要在提供审查之前确定与其利益相符的PR。默认情况下，将PRS安排在显示PRS标题的列表视图中。因此，希望拥有一个精确和简洁的标题，这对审阅者和其他开发人员都是有益的。但是，通常情况下，开发人员没有提供良好的头衔。我们发现，许多现有的公关标题的长度是不合适的（即太短或太长），或者无法传达有用的信息，这可能导致PR被忽略或拒绝。因此，需要自动技术来帮助开发人员起草高质量的头衔。 在本文中，我们介绍了自动生成PR标题的任务。我们将任务作为一句摘要任务。为了促进有关此任务的研究，我们构建了一个由495个GitHub存储库中的43,816个PR组成的数据集。我们评估了自动PR标题生成任务的最新摘要方法。我们利用胭脂指标自动评估摘要方法并进行手动评估。实验结果表明，BART分别为47.22、25.27和43.12的Rouge-1，Rouge-2和Rouge-L F1分数生成令人满意的PR标题的最佳技术。手动评估还表明，巴特产生的标题是首选。
Pull Requests (PRs) are a mechanism on modern collaborative coding platforms, such as GitHub. PRs allow developers to tell others that their code changes are available for merging into another branch in a repository. A PR needs to be reviewed and approved by the core team of the repository before the changes are merged into the branch. Usually, reviewers need to identify a PR that is in line with their interests before providing a review. By default, PRs are arranged in a list view that shows the titles of PRs. Therefore, it is desirable to have a precise and concise title, which is beneficial for both reviewers and other developers. However, it is often the case that developers do not provide good titles; we find that many existing PR titles are either inappropriate in length (i.e., too short or too long) or fail to convey useful information, which may result in PR being ignored or rejected. Therefore, there is a need for automatic techniques to help developers draft high-quality titles. In this paper, we introduce the task of automatic generation of PR titles. We formulate the task as a one-sentence summarization task. To facilitate the research on this task, we construct a dataset that consists of 43,816 PRs from 495 GitHub repositories. We evaluated the state-of-the-art summarization approaches for the automatic PR title generation task. We leverage ROUGE metrics to automatically evaluate the summarization approaches and conduct a manual evaluation. The experimental results indicate that BART is the best technique for generating satisfactory PR titles with ROUGE-1, ROUGE-2, and ROUGE-L F1-scores of 47.22, 25.27, and 43.12, respectively. The manual evaluation also shows that the titles generated by BART are preferred.
