  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.
