Exploring the Jupyter Ecosystem: An Empirical Study of Bugs and Vulnerabilities

TL;DR

This paper presents an extensive empirical analysis of bugs and vulnerabilities in the Jupyter Notebook ecosystem, revealing common issues like configuration errors and API misuse, and assessing security risks in deployment frameworks.

Contribution

It offers the first large-scale empirical study of Jupyter Notebook bugs and vulnerabilities, including a bug taxonomy and security risk assessment.

Findings

Configuration issues are the most common bugs.

Incorrect API usage frequently occurs in notebooks.

Security vulnerabilities are linked to deployment frameworks.

Abstract

Background. Jupyter notebooks are one of the main tools used by data scientists. Notebooks include features (configuration scripts, markdown, images, etc.) that make them challenging to analyze compared to traditional software. As a result, existing software engineering models, tools, and studies do not capture the uniqueness of Notebook's behavior. Aims. This paper aims to provide a large-scale empirical study of bugs and vulnerabilities in the Notebook ecosystem. Method. We collected and analyzed a large dataset of Notebooks from two major platforms. Our methodology involved quantitative analyses of notebook characteristics (such as complexity metrics, contributor activity, and documentation) to identify factors correlated with bugs. Additionally, we conducted a qualitative study using grounded theory to categorize notebook bugs, resulting in a comprehensive bug taxonomy. Finally, we analyzed security-related commits and vulnerability reports to assess risks associated with Notebook deployment frameworks. Results. Our findings highlight that configuration issues are among the most common bugs in notebook documents, followed by incorrect API usage. Finally, we explore common vulnerabilities associated with popular deployment frameworks to better understand risks associated with Notebook development. Conclusions. This work highlights that notebooks are less well-supported than traditional software, resulting in more complex code, misconfiguration, and poor maintenance.