Untangling Knots: Leveraging LLM for Error Resolution in Computational Notebooks
Fuente:
arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866916232871018496 |
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| author | Grotov, Konstantin Titov, Sergey Zharov, Yaroslav Bryksin, Timofey |
| author_facet | Grotov, Konstantin Titov, Sergey Zharov, Yaroslav Bryksin, Timofey |
| contents | Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these benefits come at the cost of reproducibility and an increased potential for bugs. There are many tools for bug fixing; however, they are generally targeted at the classical linear code. With the rise of code-fluent Large Language Models, a new stream of smart bug-fixing tools has emerged. However, the applicability of those tools is still problematic for non-linear computational notebooks. In this paper, we propose a potential solution for resolving errors in computational notebooks via an iterative LLM-based agent. We discuss the questions raised by this approach and share a novel dataset of computational notebooks containing bugs to facilitate the research of the proposed approach. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_01559 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Untangling Knots: Leveraging LLM for Error Resolution in Computational Notebooks Grotov, Konstantin Titov, Sergey Zharov, Yaroslav Bryksin, Timofey Software Engineering Machine Learning Computational notebooks became indispensable tools for research-related development, offering unprecedented interactivity and flexibility in the development process. However, these benefits come at the cost of reproducibility and an increased potential for bugs. There are many tools for bug fixing; however, they are generally targeted at the classical linear code. With the rise of code-fluent Large Language Models, a new stream of smart bug-fixing tools has emerged. However, the applicability of those tools is still problematic for non-linear computational notebooks. In this paper, we propose a potential solution for resolving errors in computational notebooks via an iterative LLM-based agent. We discuss the questions raised by this approach and share a novel dataset of computational notebooks containing bugs to facilitate the research of the proposed approach. |
| title | Untangling Knots: Leveraging LLM for Error Resolution in Computational Notebooks |
| topic | Software Engineering Machine Learning |
| url | https://arxiv.org/abs/2405.01559 |