With the rise of the pull request mechanism in software development, the quality of pull requests has gained more attention. Prior works focus on improving the quality of pull request descriptions and several approaches have been proposed to automatically generate pull request descriptions. As an essential component of a pull request, pull request titles have not received a similar level of attention. To further facilitate automation in software development and to help developers in drafting high-quality pull request titles, we introduce AutoPRTitle. AutoPRTitle is specifically designed to automatically generate pull request titles. AutoPRTitle can generate a precise and succinct pull request title based on the pull request description, commit messages, and the associated issue titles. AutoPRTitle is built upon a state-of-the-art text summarization model, BART, which has been pre-trained on large-scale English corpora. We further fine-tuned BART in a pull request dataset containing high-quality pull request titles. We implemented AutoPRTitle as a stand-alone web application. We conducted two sets of evaluations: one concerning the model accuracy and the other concerning the tool usability. For model accuracy, BART outperforms the best baseline by 24.6%, 40.5%, and 23.3%, respectively. For tool usability, the evaluators consider our tool as easy-to-use and useful when creating a pull request title of good quality. Source code: https://github.com/soarsmu/Auto-PR-Title Video demo: https://tinyurl.com/AutoPRTitle
翻译:随着软件开发的拉动请求机制的提高,拉动请求的质量得到了更多的关注。先前的工作重点是提高拉动请求描述的质量,并提出了若干自动生成拉动请求描述的方法。作为拉动请求的一个基本组成部分,拉动请求标题没有得到类似的关注。为了进一步促进软件开发自动化,并帮助开发者起草高质量的拉动请求标题,我们引入了AutoPRTitle。AutoPRTitle是专门设计自动生成拉动请求标题的。AutoPRTitle可以产生精确和简洁的拉动请求标题,根据拉动请求描述、发送信息及相关议题标题。AutoPRTitle建在最先进的文本拼凑模型中,BARTI(对大型英国公司进行了预先培训)没有受到类似的关注。为了进一步便利软件开发的自动化请求,帮助开发者起草高质量的拉动请求标题。我们实施了AutoPRTetle作为独立的网络应用程序。我们进行了两套评估:关于模型准确性和工具可用性。关于模型的精确性,BARTS-perutoal-ruetal A 建于模型精度:BAR-pera-rubal-rubal-rubal A 将最佳基线设定设定纳入 236, 考虑我们的最佳评估工具使用。