Better Python Programming for all: With the focus on Maintainability
Fuente:
arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| author | Shivashankar, Karthik Martini, Antonio |
| author_facet | Shivashankar, Karthik Martini, Antonio |
| contents | This study aims to enhance the maintainability of code generated by Large Language Models (LLMs), with a focus on the Python programming language. As the use of LLMs for coding assistance grows, so do concerns about the maintainability of the code they produce. Previous research has mainly concentrated on the functional accuracy and testing success of generated code, overlooking aspects of maintainability.
Our approach involves the use of a specifically designed dataset for training and evaluating the model, ensuring a thorough assessment of code maintainability. At the heart of our work is the fine-tuning of an LLM for code refactoring, aimed at enhancing code readability, reducing complexity, and improving overall maintainability.
After fine-tuning an LLM to prioritize code maintainability, our evaluations indicate that this model significantly improves code maintainability standards, suggesting a promising direction for the future of AI-assisted software development. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_09134 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Better Python Programming for all: With the focus on Maintainability Shivashankar, Karthik Martini, Antonio Software Engineering Artificial Intelligence This study aims to enhance the maintainability of code generated by Large Language Models (LLMs), with a focus on the Python programming language. As the use of LLMs for coding assistance grows, so do concerns about the maintainability of the code they produce. Previous research has mainly concentrated on the functional accuracy and testing success of generated code, overlooking aspects of maintainability. Our approach involves the use of a specifically designed dataset for training and evaluating the model, ensuring a thorough assessment of code maintainability. At the heart of our work is the fine-tuning of an LLM for code refactoring, aimed at enhancing code readability, reducing complexity, and improving overall maintainability. After fine-tuning an LLM to prioritize code maintainability, our evaluations indicate that this model significantly improves code maintainability standards, suggesting a promising direction for the future of AI-assisted software development. |
| title | Better Python Programming for all: With the focus on Maintainability |
| topic | Software Engineering Artificial Intelligence |
| url | https://arxiv.org/abs/2408.09134 |