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

• URL: http://arxiv.org/abs/2507.18833v1
• Date: Thu, 24 Jul 2025 22:09:21 GMT
• Title: Exploring the Jupyter Ecosystem: An Empirical Study of Bugs and Vulnerabilities
• Authors: Wenyuan Jiang, Diany Pressato, Harsh Darji, Thibaud Lutellier,
• Abstract summary: This paper aims to provide a large-scale empirical study of bugs and vulnerabilities in the Notebook ecosystem.<n>We collected and analyzed a large dataset of Notebooks from two major platforms.
• Score: 3.4769545753909608
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• A Systematic Literature Review of Software Engineering Research on Jupyter Notebook [8.539234346904905]
  The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks.<n>The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction.
  arXiv  Detail & Related papers  (2025-04-22T18:12:04Z)
• An Empirical Study on the Classification of Bug Reports with Machine Learning [1.1499574149885023]
  We study how different factors (e.g., project language, report content) can influence the performance of models in handling classification of issue reports.<n>Using the report title or description does not significantly differ; Support Vector Machine, Logistic Regression, and Random Forest are effective in classifying issue reports.<n>Models based on heterogeneous projects can classify reports from projects not present during training.
  arXiv  Detail & Related papers  (2025-03-01T23:19:56Z)
• Debug Smarter, Not Harder: AI Agents for Error Resolution in Computational Notebooks [4.025358960630117]
  We present an AI agent designed specifically for error resolution in a computational notebook. We have developed an agentic system capable of exploring a notebook environment by interacting with it. We evaluate our approach against the pre-existing single-action solution by comparing costs and conducting a user study.
  arXiv  Detail & Related papers  (2024-10-18T11:55:34Z)
• Spurious Correlations in Machine Learning: A Survey [27.949532561102206]
  Machine learning systems are sensitive to spurious correlations between non-essential features of the inputs and labels. These features and their correlations with the labels are known as "spurious" because they tend to change with shifts in real-world data distributions. We provide a review of this issue, along with a taxonomy of current state-of-the-art methods for addressing spurious correlations in machine learning models.
  arXiv  Detail & Related papers  (2024-02-20T04:49:34Z)
• EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis [48.5877840394508]
  In recent years there has been a shift from quantifications-based malware detection towards machine learning. We propose to address the deficiencies in the space of similarity research on binary files, starting from EMBER. We enhance EMBER with similarity information as well as malware class tags, to enable further research in the similarity space.
  arXiv  Detail & Related papers  (2023-10-03T06:58:45Z)
• PyRCA: A Library for Metric-based Root Cause Analysis [66.72542200701807]
  PyRCA is an open-source machine learning library of Root Cause Analysis (RCA) for Artificial Intelligence for IT Operations (AIOps) It provides a holistic framework to uncover the complicated metric causal dependencies and automatically locate root causes of incidents.
  arXiv  Detail & Related papers  (2023-06-20T09:55:10Z)
• Mining the Characteristics of Jupyter Notebooks in Data Science Projects [1.655246222110267]
  The computational notebook (e.g., Jupyter Notebook) is a well-known data science tool adopted in practice.<n>This research aims to understand the characteristics of high-voted Jupyter Notebooks on Kaggle and the popular Jupyter Notebooks for data science projects on GitHub.
  arXiv  Detail & Related papers  (2023-04-11T16:30:53Z)
• SmartBook: AI-Assisted Situation Report Generation for Intelligence Analysts [55.73424958012229]
  This work identifies intelligence analysts' practices and preferences for AI assistance in situation report generation. We introduce SmartBook, an automated framework designed to generate situation reports from large volumes of news data. Our comprehensive evaluation of SmartBook, encompassing a user study alongside a content review with an editing study, reveals SmartBook's effectiveness in generating accurate and relevant situation reports.
  arXiv  Detail & Related papers  (2023-03-25T03:03:00Z)
• Annotation Error Detection: Analyzing the Past and Present for a More Coherent Future [63.99570204416711]
  We reimplement 18 methods for detecting potential annotation errors and evaluate them on 9 English datasets. We define a uniform evaluation setup including a new formalization of the annotation error detection task. We release our datasets and implementations in an easy-to-use and open source software package.
  arXiv  Detail & Related papers  (2022-06-05T22:31:45Z)
• Early Detection of Security-Relevant Bug Reports using Machine Learning: How Far Are We? [6.438136820117887]
  In a typical maintenance scenario, security-relevant bug reports are prioritised by the development team when preparing corrective patches. Open security-relevant bug reports can become a critical leak of sensitive information that attackers can leverage to perform zero-day attacks. In recent years, approaches for the detection of security-relevant bug reports based on machine learning have been reported with promising performance.
  arXiv  Detail & Related papers  (2021-12-19T11:30:29Z)
• S3M: Siamese Stack (Trace) Similarity Measure [55.58269472099399]
  We present S3M -- the first approach to computing stack trace similarity based on deep learning. It is based on a biLSTM encoder and a fully-connected classifier to compute similarity. Our experiments demonstrate the superiority of our approach over the state-of-the-art on both open-sourced data and a private JetBrains dataset.
  arXiv  Detail & Related papers  (2021-03-18T21:10:41Z)
• A Survey of Adversarial Learning on Graphs [59.21341359399431]
  We investigate and summarize the existing works on graph adversarial learning tasks. Specifically, we survey and unify the existing works w.r.t. attack and defense in graph analysis tasks. We emphasize the importance of related evaluation metrics, investigate and summarize them comprehensively.
  arXiv  Detail & Related papers  (2020-03-10T12:48:00Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.