Bug Analysis in Jupyter Notebook Projects: An Empirical Study

TL;DR

This paper provides a comprehensive empirical analysis of bugs in Jupyter Notebook projects, based on large-scale data mining, practitioner interviews, and bug taxonomy development, revealing key challenges faced by data scientists.

Contribution

It introduces a detailed bug taxonomy for Jupyter projects and offers insights into common bug categories, root causes, and development challenges from practitioners' perspectives.

Findings

Identified prevalent bug categories and their root causes.

Revealed common development challenges faced by Jupyter practitioners.

Proposed a taxonomy to classify Jupyter bugs systematically.

Abstract

Computational notebooks, such as Jupyter, have been widely adopted by data scientists to write code for analyzing and visualizing data. Despite their growing adoption and popularity, there has been no thorough study to understand Jupyter development challenges from the practitioners' point of view. This paper presents a systematic study of bugs and challenges that Jupyter practitioners face through a large-scale empirical investigation. We mined 14,740 commits from 105 GitHub open-source projects with Jupyter notebook code. Next, we analyzed 30,416 Stack Overflow posts which gave us insights into bugs that practitioners face when developing Jupyter notebook projects. Finally, we conducted nineteen interviews with data scientists to uncover more details about Jupyter bugs and to gain insights into Jupyter developers' challenges. We propose a bug taxonomy for Jupyter projects based on our results. We also highlight bug categories, their root causes, and the challenges that Jupyter practitioners face.