Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approach
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arXiv
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866909963481251840 |
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| author | Chakrabarty, Sayak Pal, Souradip |
| author_facet | Chakrabarty, Sayak Pal, Souradip |
| contents | Automated documentation of programming source code is a challenging task with significant practical and scientific implications for the developer community. We present a large language model (LLM)-based application that developers can use as a support tool to generate basic documentation for any publicly available repository. Over the last decade, several papers have been written on generating documentation for source code using neural network architectures. With the recent advancements in LLM technology, some open-source applications have been developed to address this problem. However, these applications typically rely on the OpenAI APIs, which incur substantial financial costs, particularly for large repositories. Moreover, none of these open-source applications offer a fine-tuned model or features to enable users to fine-tune. Additionally, finding suitable data for fine-tuning is often challenging. Our application addresses these issues which is available at https://pypi.org/project/readme-ready/. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_00726 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approach Chakrabarty, Sayak Pal, Souradip Software Engineering Artificial Intelligence Machine Learning Automated documentation of programming source code is a challenging task with significant practical and scientific implications for the developer community. We present a large language model (LLM)-based application that developers can use as a support tool to generate basic documentation for any publicly available repository. Over the last decade, several papers have been written on generating documentation for source code using neural network architectures. With the recent advancements in LLM technology, some open-source applications have been developed to address this problem. However, these applications typically rely on the OpenAI APIs, which incur substantial financial costs, particularly for large repositories. Moreover, none of these open-source applications offer a fine-tuned model or features to enable users to fine-tune. Additionally, finding suitable data for fine-tuning is often challenging. Our application addresses these issues which is available at https://pypi.org/project/readme-ready/. |
| title | Free and Customizable Code Documentation with LLMs: A Fine-Tuning Approach |
| topic | Software Engineering Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.00726 |