Eliciting Best Practices for Collaboration with Computational Notebooks

TL;DR

This paper identifies and evaluates best practices for collaborative data science using computational notebooks, highlighting gaps between awareness and consistent adoption due to tool limitations.

Contribution

It provides a catalog of best practices for collaborative notebook use, assesses their awareness among data scientists, and analyzes real-world adoption patterns.

Findings

Experts are aware of most best practices.

Adoption varies depending on context and tool support.

Some best practices are often unfeasible or counterproductive.

Abstract

Despite the widespread adoption of computational notebooks, little is known about best practices for their usage in collaborative contexts. In this paper, we fill this gap by eliciting a catalog of best practices for collaborative data science with computational notebooks. With this aim, we first look for best practices through a multivocal literature review. Then, we conduct interviews with professional data scientists to assess their awareness of these best practices. Finally, we assess the adoption of best practices through the analysis of 1,380 Jupyter notebooks retrieved from the Kaggle platform. Findings reveal that experts are mostly aware of the best practices and tend to adopt them in their daily work. Nonetheless, they do not consistently follow all the recommendations as, depending on specific contexts, some are deemed unfeasible or counterproductive due to the lack of proper tool support. As such, we envision the design of notebook solutions that allow data scientists not to have to prioritize exploration and rapid prototyping over writing code of quality.