Summary of Automatic Pull Request Description Generation Using Llms: a T5 Model Approach, by Md Nazmus Sakib et al.
Automatic Pull Request Description Generation Using LLMs: A T5 Model Approach
by Md Nazmus Sakib, Md Athikul Islam, Md Mashrur Arifin
First submitted to arxiv on: 1 Aug 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL); Software Engineering (cs.SE)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes an automated method to generate pull request (PR) descriptions based on commit messages and source code comments. This method frames the task as a text summarization problem and utilizes the T5 text-to-text transfer model, fine-tuned using a dataset containing 33,466 PRs. The model’s effectiveness is assessed using ROUGE metrics, which are recognized for their strong alignment with human evaluations. The results show that the T5 model significantly outperforms LexRank, serving as our baseline. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps develop better ways to explain what changes were made in computer code updates called pull requests. Pull request descriptions help others understand these updates and make sure they are correct. Sometimes developers forget to write these descriptions, which can cause problems. To solve this problem, the researchers created a way for computers to automatically generate these descriptions using information from earlier code comments and messages. |
Keywords
* Artificial intelligence * Alignment * Rouge * Summarization * T5
