一句话：该论文验证在计算笔记本文档生成中引入代码度量可显著提升深度学习模型准确率。
论文关注计算笔记本中代码文档常被忽视、手工撰写成本高的问题，研究自动文档生成。作者构建了 22K 个代码-文档对的数据集，在深度学习文档生成模型中加入代码度量特征，如代码行数、API 流行度和复杂度，并与未增强模型对比。结果表明，融入这些代码度量能显著提高文档生成的准确率，说明代码度量与高质量文档之间存在紧密联系，为计算笔记本的自动化文档工具提供了新思路。
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.
DOI 原文 ·
@inproceedings{paperbot8297,
title = {Can Code Metrics Enhance Documentation Generation for Computational Notebooks?},
author = {Mojtaba Mostafavi Ghahfarokhi and Hamed Jahantigh and Sepehr Kianiangolafshani and Ashkan Khademian and Alireza Asadi and Abbas Heydarnoori},
booktitle = {ASE},
year = {2024},
doi = {10.1145/3691620.3695334}
}