A roadmap for enriching jupyter notebooks documentation with kaggle data

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Mostafavi Ghahfarokhi, M ; Sharif University of Technology | 2024

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Type of Document: Article
DOI: 10.1145/3644815.3644984
Publisher: 2024
Abstract:
Recent advancements in AI and data science have led to the increased use of Jupyter notebooks. As such, various AI-Based automated tools have been also developed to automatically document notebooks. However, a key challenge is the absence of suitable datasets for training AI models. In this paper, we outline a valuable roadmap for developing a dataset of (markdown, code) pairs centered on functions in Jupyter notebooks. The roadmap encompasses four high-level steps: structural filtering, structural processing, conceptual filtering, and conceptual processing. Our proposed roadmap leads to providing a quality dataset for training AI models on Jupyter notebooks. © 2024 Copyright held by the owner/author(s)
Keywords:
Jupyter notebooks ; Kaggle dataset ; Automated tools ; Conceptual processing ; Filtering processing ; Jupyter notebook ; Kaggle dataset ; Level steps ; Markdown generation ; Roadmap ; Structural processing
Source: Proceedings - 2024 IEEE/ACM 3rd International Conference on AI Engineering - Software Engineering for AI, CAIN 2024 ; 2024 , Pages 271-272 ; 979-840070591-5 (ISBN)
URL: https://ieeexplore.ieee.org/document/10556010