Beyond Syntax: Unleashing the Power of Computational Notebooks Code Metrics in Documentation Generation

Author:

Mostafavi Ghahfarokhi Mojtaba1ORCID,Khademian Ashkan1ORCID,Kianiangolafshani Sepehr1ORCID,Asadi Alireza1ORCID,Jahantigh Hamed1ORCID,Heydarnoori Abbas1ORCID

Affiliation:

1. Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

Publisher

ACM

Reference11 articles.

1. Documentation Matters: Human-Centered AI System to Assist Data Science Code Documentation in Computational Notebooks

2. Xuye Liu, Dakuo Wang, April Wang, Yufang Hou, and Lingfei Wu. Haconvgnn: Hierarchical attention based convolutional graph neural network for code documentation generation in jupyter notebooks. arXiv:2104.01002 (2021, 2021.

3. Daya Guo, Shuo Ren, Shuai Lu, Zhangyin Feng, Duyu Tang, Shujie LIU, Long Zhou, Nan Duan, Alexey Svyatkovskiy, Shengyu Fu, Michele Tufano, Shao Kun Deng, Colin Clement, Dawn Drain, Neel Sundaresan, Jian Yin, Daxin Jiang, and Ming Zhou. Graphcode{bert}: Pre-training code representations with data flow. In International Conference on Learning Representations, 2021.

4. A large-scale comparison of Python code in Jupyter notebooks and scripts

5. Automatically Assessing Code Understandability

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