AI-Mediated Code Comment Improvement
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Online Access: | |
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| _version_ | 1866910943178391552 |
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| author | Dhakal, Maria Su, Chia-Yi Wallace, Robert Fakhimi, Chris Bansal, Aakash Li, Toby Huang, Yu McMillan, Collin |
| author_facet | Dhakal, Maria Su, Chia-Yi Wallace, Robert Fakhimi, Chris Bansal, Aakash Li, Toby Huang, Yu McMillan, Collin |
| contents | This paper describes an approach to improve code comments along different quality axes by rewriting those comments with customized Artificial Intelligence (AI)-based tools. We conduct an empirical study followed by grounded theory qualitative analysis to determine the quality axes to improve. Then we propose a procedure using a Large Language Model (LLM) to rewrite existing code comments along the quality axes. We implement our procedure using GPT-4o, then distil the results into a smaller model capable of being run in-house, so users can maintain data custody. We evaluate both our approach using GPT-4o and the distilled model versions. We show in an evaluation how our procedure improves code comments along the quality axes. We release all data and source code in an online repository for reproducibility. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_09021 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI-Mediated Code Comment Improvement Dhakal, Maria Su, Chia-Yi Wallace, Robert Fakhimi, Chris Bansal, Aakash Li, Toby Huang, Yu McMillan, Collin Software Engineering Artificial Intelligence Programming Languages This paper describes an approach to improve code comments along different quality axes by rewriting those comments with customized Artificial Intelligence (AI)-based tools. We conduct an empirical study followed by grounded theory qualitative analysis to determine the quality axes to improve. Then we propose a procedure using a Large Language Model (LLM) to rewrite existing code comments along the quality axes. We implement our procedure using GPT-4o, then distil the results into a smaller model capable of being run in-house, so users can maintain data custody. We evaluate both our approach using GPT-4o and the distilled model versions. We show in an evaluation how our procedure improves code comments along the quality axes. We release all data and source code in an online repository for reproducibility. |
| title | AI-Mediated Code Comment Improvement |
| topic | Software Engineering Artificial Intelligence Programming Languages |
| url | https://arxiv.org/abs/2505.09021 |