Can Code Metrics Enhance Documentation Generation for Computational Notebooks?

In software development, code documentation is crucial for collaboration and maintenance, especially as projects become more complex. However, it is often neglected due to the tedious effort it requires. This paper explores automating documentation generation for computational notebooks, focusing on...

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Bibliographic Details
Published in:IEEE/ACM International Conference on Automated Software Engineering : [proceedings] pp. 2472 - 2473
Main Authors: Ghahfarokhi, Mojtaba Mostafavi, Jahantigh, Hamed, Kianiangolafshani, Sepehr, Khademian, Ashkan, Asadi, Alireza, Heydarnoori, Abbas
Format: Conference Proceeding
Language:English
Published: ACM 27.10.2024
Subjects:
Code metrics
Codes
Complexity theory
Computational modeling
Deep learning
Deep learning models
Documentation
Focusing
Jupyter notebooks
Maintenance
Measurement
Software development management
Software engineering
ISSN:2643-1572
Online Access:Get full text
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Summary:In software development, code documentation is crucial for collaboration and maintenance, especially as projects become more complex. However, it is often neglected due to the tedious effort it requires. This paper explores automating documentation generation for computational notebooks, focusing on the impact of code metrics such as lines of code, API popularity, and complexity on this task. Using a dataset of 22K code-documentation pairs, we compare deep learning models with and without code metric augmentation. The results show that incorporating these metrics significantly improves the accuracy of documentation generation, underscoring the connection between code metrics and quality documentation.
ISSN:2643-1572
DOI:10.1145/3691620.3695334