Automatic Pull Request Description Generation Using LLMs: A T5 Model Approach
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866911975141801984 |
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| author | Sakib, Md Nazmus Islam, Md Athikul Arifin, Md Mashrur |
| author_facet | Sakib, Md Nazmus Islam, Md Athikul Arifin, Md Mashrur |
| contents | Developers create pull request (PR) descriptions to provide an overview of their changes and explain the motivations behind them. These descriptions help reviewers and fellow developers quickly understand the updates. Despite their importance, some developers omit these descriptions. To tackle this problem, we propose an automated method for generating PR descriptions based on commit messages and source code comments. This method frames the task as a text summarization problem, for which we utilized the T5 text-to-text transfer model. We fine-tuned a pre-trained T5 model using a dataset containing 33,466 PRs. The model's effectiveness was assessed using ROUGE metrics, which are recognized for their strong alignment with human evaluations. Our findings reveal that the T5 model significantly outperforms LexRank, which served as our baseline for comparison. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_00921 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Automatic Pull Request Description Generation Using LLMs: A T5 Model Approach Sakib, Md Nazmus Islam, Md Athikul Arifin, Md Mashrur Machine Learning Computation and Language Software Engineering Developers create pull request (PR) descriptions to provide an overview of their changes and explain the motivations behind them. These descriptions help reviewers and fellow developers quickly understand the updates. Despite their importance, some developers omit these descriptions. To tackle this problem, we propose an automated method for generating PR descriptions based on commit messages and source code comments. This method frames the task as a text summarization problem, for which we utilized the T5 text-to-text transfer model. We fine-tuned a pre-trained T5 model using a dataset containing 33,466 PRs. The model's effectiveness was assessed using ROUGE metrics, which are recognized for their strong alignment with human evaluations. Our findings reveal that the T5 model significantly outperforms LexRank, which served as our baseline for comparison. |
| title | Automatic Pull Request Description Generation Using LLMs: A T5 Model Approach |
| topic | Machine Learning Computation and Language Software Engineering |
| url | https://arxiv.org/abs/2408.00921 |