JupOtter: Cell-Level Bug Detection in Jupyter Notebooks

• URL: http://arxiv.org/abs/2606.23877v1
• Date: Mon, 22 Jun 2026 19:23:59 GMT
• Title: JupOtter: Cell-Level Bug Detection in Jupyter Notebooks
• Abstract summary: We present JupOtter, a bug detection system designed specifically for Jupyter Notebooks.<n>JupOtter features three novel contributions: (1) a notebook-specific tokenization strategy that preserves cell structure, (2) a cell-level bug prediction technique, and (3) a new labeled dataset, OtterDataset, containing over 21,000 notebooks annotated for fine-grained cell-level bug detection.
• Score: 2.041585741962914
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: Jupyter Notebooks are an increasingly popular coding environment used across many domains, especially in Python-based data science and scientific computing. Originally used for prototyping and interactive exploration, notebooks are increasingly used to develop more complex programs, leading to a rapid rise in buggy notebooks on platforms like GitHub. To address this trend, we present JupOtter, a bug detection system designed specifically for Jupyter Notebooks. JupOtter features three novel contributions: (1) a notebook-specific tokenization strategy that preserves cell structure, (2) a cell-level bug prediction technique, and (3) a new labeled dataset, OtterDataset, containing over 21,000 notebooks annotated for fine-grained cell-level bug detection. JupOtter achieves cell-level bug detection F1 scores that surpass static analyzers and large language models in two out of three evaluation datasets.

Related papers

• Analysing Python Machine Learning Notebooks with Moose [0.12249546377051435]
  Machine Learning (ML) code, particularly within notebooks, often exhibits lower quality compared to traditional software.<n>Bad practices arise at three distinct levels: general Python coding conventions, the organizational structure of the notebook itself, and ML-specific aspects such as API usage.<n>This paper introduces Vespucci Linter, a static analysis tool with multi-level capabilities, built on Moose.
  arXiv  Detail & Related papers  (2025-09-15T09:59:49Z)
• Exploring the Jupyter Ecosystem: An Empirical Study of Bugs and Vulnerabilities [3.4769545753909608]
  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.
  arXiv  Detail & Related papers  (2025-07-24T22:09:21Z)
• Observing Fine-Grained Changes in Jupyter Notebooks During Development Time [12.75622665542759]
  We introduce a toolset for collecting code changes in Jupyter notebooks during development time.<n>We then use it to collect more than 100 hours of work related to a data analysis task and a machine learning task.<n>In our analysis of the collected data, we classified the changes made to the cells between executions and found that a significant number of these changes were code modifications.
  arXiv  Detail & Related papers  (2025-07-21T17:41:51Z)
• Rethinking End-to-End 2D to 3D Scene Segmentation in Gaussian Splatting [86.15347226865826]
  We design a new end-to-end object-aware lifting approach, named Unified-Lift.<n>We augment each Gaussian point with an additional Gaussian-level feature learned using a contrastive loss to encode instance information.<n>We conduct experiments on three benchmarks: LERF-Masked, Replica, and Messy Rooms.
  arXiv  Detail & Related papers  (2025-03-18T08:42:23Z)
• Suggesting Code Edits in Interactive Machine Learning Notebooks Using Large Language Models [3.2433570328895196]
  We present the first dataset of 48,398 Jupyter notebook edits derived from 20,095 revisions of 792 machine learning repositories on GitHub.<n>Our dataset captures granular details of cell-level and line-level modifications, offering a foundation for understanding real-world maintenance patterns in machine learning.
  arXiv  Detail & Related papers  (2025-01-16T18:55:38Z)
• DetCLIPv3: Towards Versatile Generative Open-vocabulary Object Detection [111.68263493302499]
  We introduce DetCLIPv3, a high-performing detector that excels at both open-vocabulary object detection and hierarchical labels. DetCLIPv3 is characterized by three core designs: 1) Versatile model architecture; 2) High information density data; and 3) Efficient training strategy. DetCLIPv3 demonstrates superior open-vocabulary detection performance, outperforming GLIPv2, GroundingDINO, and DetCLIPv2 by 18.0/19.6/6.6 AP, respectively.
  arXiv  Detail & Related papers  (2024-04-14T11:01:44Z)
• Multi-Label Meta Weighting for Long-Tailed Dynamic Scene Graph Generation [55.429541407920304]
  Recognizing the predicate between subject and object pairs is imbalanced and multi-label in nature. Recent state-of-the-art methods predominantly focus on the most frequently occurring predicate classes. We introduce a multi-label meta-learning framework to deal with the biased predicate distribution.
  arXiv  Detail & Related papers  (2023-06-16T18:14:23Z)
• 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)
• Deep learning for table detection and structure recognition: A survey [49.09628624903334]
  The goal of this survey is to provide a profound comprehension of the major developments in the field of Table Detection. We provide an analysis of both classic and new applications in the field. The datasets and source code of the existing models are organized to provide the reader with a compass on this vast literature.
  arXiv  Detail & Related papers  (2022-11-15T19:42:27Z)
• HAConvGNN: Hierarchical Attention Based Convolutional Graph Neural Network for Code Documentation Generation in Jupyter Notebooks [33.37494243822309]
  We propose a hierarchical attention-based ConvGNN component to augment the Seq2Seq network. We build a dataset with publicly available Kaggle notebooks and evaluate our model (HAConvGNN) against baseline models.
  arXiv  Detail & Related papers  (2021-03-31T22:36:41Z)
• 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)
• Superiority of Simplicity: A Lightweight Model for Network Device Workload Prediction [58.98112070128482]
  We propose a lightweight solution for series prediction based on historic observations. It consists of a heterogeneous ensemble method composed of two models - a neural network and a mean predictor. It achieves an overall $R2$ score of 0.10 on the available FedCSIS 2020 challenge dataset.
  arXiv  Detail & Related papers  (2020-07-07T15:44:16Z)

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.