Abstract:In software engineering research, the analysis of fine-grained logs led to significant innovations in areas such as refactoring, security, and code completion. However, even though computational notebooks are a staple of data science and an important tool in machine learning, few similar studies have been conducted in this area.
To help bridge this research gap, this paper makes three scientific contributions. (1) We introduce a toolset for collecting code changes in Jupyter notebooks during development time. (2) We use it to collect more than 100 hours of work related to a data analysis task and a machine learning task (carried out by 20 developers with different levels of expertise), resulting in a dataset containing 2,655 cells and 9,207 cell executions. (3) Finally, we use this dataset to investigate the dynamic nature of the notebook development process and the changes that take place in the notebooks.
In our analysis of the collected data, we classified the changes made to the cells between executions and found that a significant number of these changes constituted code iteration modifications. We report a number of other insights and propose detailed future research directions on the novel data.
From: Yaroslav Golubev [view email]
[v1]
Mon, 21 Jul 2025 17:41:51 UTC (263 KB)
[v2]
Thu, 24 Jul 2025 22:24:46 UTC (263 KB)
[v3]
Thu, 2 Jul 2026 18:02:04 UTC (362 KB)
此内容由惯性聚合(RSS阅读器)自动聚合整理，仅供阅读参考。 原文来自 — 版权归原作者所有。