AutoPRTitle: A Tool for Automatic Pull Request Title Generation
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Abstract (arXiv)
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
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 23 Jun 2022.
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Citing papers per year: 2024: 2; 2025: 1; 2026: 1.
4 papers.
- 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHubcited by 53
- 2024Automatic Programming: Large Language Models and Beyondcited by 35
- 2024From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptionscited by 16
- 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Reviewcited by 1
- 2026Rethinking Code Review in the Age of AI: A Vision for Agentic Code Review17 May 2026
- 2025On the Use of Agentic Coding: An Empirical Study of Pull Requests on GitHub18 Sep 2025
- 2024Automatic Programming: Large Language Models and Beyond3 May 2024
- 2024From Text Segmentation to Smart Chaptering: A Novel Benchmark for Structuring Video Transcriptions27 Feb 2024