AUTOPRTITLE: A tool for automatic pull request title generation

• Main Authors: IRSAN, Ivana Clairine, ZHANG, Ting, THUNG, Ferdian, LO, David, JIANG, Lingxiao
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2022
• Subjects: Pull request; GitHub; Summarization; Pretrained models; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/7700; https://ink.library.smu.edu.sg/context/sis_research/article/8703/viewcontent/autoprtitle.pdf
• Institution: Singapore Management University

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 draft high-quality pull request titles, we introduce AutoPRTitle. AutoPRTitle is specifically designed to generate pull request titles automatically. 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.