# GitBugs: Bug Reports for Duplicate Detection, Retrieval Augmented Generation, Triage, and More

arXiv: https://arxiv.org/abs/2504.09651 · HTML: https://mlsanity.com/paper/2504.09651/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Apr 2025.

## Built on by (most cited first, 3 of 3)

- [When Gradients Collide: Failure Modes of Multi-Objective Prompt Optimization for LLM Judges](https://mlsanity.com/paper/2605.26046/) (2026)
- [Automated Root-Cause Subclassification and No-Code Fix Generation for Invalid Bug Reports](https://mlsanity.com/paper/2605.17561/) (2026)
- [When Bugs Linger: A Study of Anomalous Resolution Time Outliers and Their Themes](https://mlsanity.com/paper/2509.16140/) (2025)

## Abstract (arXiv)

Bug reports provide critical insights into software quality, yet existing datasets often suffer from limited scope, outdated content, or insufficient metadata for machine learning. To address these limitations, we present GitBugs-a comprehensive and up-to-date dataset comprising over 150,000 bug reports from nine actively maintained open-source projects, including Firefox, Cassandra, and VS Code. GitBugs aggregates data from Github, Bugzilla and Jira issue trackers, offering standardized categorical fields for classification tasks and predefined train/test splits for duplicate bug detection. In addition, it includes exploratory analysis notebooks and detailed project-level statistics, such as duplicate rates and resolution times. GitBugs supports various software engineering research tasks, including duplicate detection, retrieval augmented generation, resolution prediction, automated triaging, and temporal analysis. The openly licensed dataset provides a valuable cross-project resource for benchmarking and advancing automated bug report analysis. Access the data and code at https://github.com/av9ash/gitbugs/.