Datasets:
metadata
license: mit
configs:
- config_name: default
data_files:
- split: split1
path: data/split1-*
- split: split2
path: data/split2-*
- split: split3
path: data/split3-*
- split: split4
path: data/split4-*
dataset_info:
features:
- name: repo
dtype: string
- name: pr_number
dtype: int64
- name: title
dtype: string
- name: body
dtype: string
- name: buggy_commit
dtype: string
- name: fix_commit
dtype: string
- name: buggy_distance
dtype: int64
- name: confidence
dtype: string
- name: files
list:
- name: filename
dtype: string
- name: patch
dtype: string
- name: additions
dtype: int64
- name: deletions
dtype: int64
splits:
- name: split1
num_bytes: 34024342
num_examples: 6000
- name: split2
num_bytes: 31467405
num_examples: 6000
- name: split3
num_bytes: 34011489
num_examples: 6000
- name: split4
num_bytes: 34819671
num_examples: 6000
download_size: 50269862
dataset_size: 134322907
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: Github issues dataset
size_categories:
- 10K<n<100K
GitHub Issues + Fixes Dataset
A curated, high-signal dataset of GitHub issues collected from 25 popular open-source repositories.
Each example pairs a real GitHub issue with the exact code changes (diffs) that resolved it.
The dataset is designed for:
- Automated bug fixing
- LLM-based code agents
- Issue → patch generation
- Program repair research
How the data was extracted
The data was collected using the GitHub REST API and processed into a structured format.
To maintain quality and usefulness:
- Only closed issues were considered
- Each issue must have a clearly associated fix
- Fixes are stored as unified diffs extracted from the resolving commit
- Low-signal issues (questions, duplicates, discussions) were filtered out
- Issues without meaningful code changes were excluded
Each row represents one issue–fix pair.
Dataset structure
Each dataset entry has the following schema:
{
"repo": "owner/repository",
"issue_number": 12345,
"issue_title": "Short description of the problem",
"issue_body": "Full issue discussion and problem description",
"commit_sha": "abcdef123456...",
"files": [
{
"filename": "path/to/file.ext",
"patch": "unified diff showing the fix",
"additions": 10,
"deletions": 2
}
]
}
| Field | Description |
|---|---|
repo |
GitHub repository where the issue originated |
issue_number |
Original GitHub issue number |
issue_title |
Title of the issue |
issue_body |
Full issue description and context |
commit_sha |
Commit that fixed the issue |
files |
List of modified files |
files[].filename |
Path of the modified file |
files[].patch |
Unified diff representing the fix |
files[].additions |
Number of added lines |
files[].deletions |
Number of removed lines |
Supported languages
The dataset contains fixes across multiple programming languages, including (but not limited to):
- C / C++
- Python
- JavaScript / TypeScript
- Rust
- Go
- Java
- Assembly (very rare)
Language distribution varies by repository.
Intended use cases
This dataset is well-suited for:
- Training models to generate code patches from issue descriptions
- Evaluating LLM reasoning over real-world bug reports
- Building autonomous debugging or refactoring agents
- Research on program repair, code synthesis, and software maintenance
It is not intended for:
- Issue classification
- sentiment analysis
- Chatbot fine-tuning without code generation
Limitations
- The dataset reflects real-world noise from GitHub issues
- Issue descriptions vary widely in clarity and detail
- Some fixes involve refactoring or design changes rather than minimal patches
- No guarantee that all fixes are optimal or best practice
Warning: This dataset currently has the issues of 10/25 repos and 14k rows but is expected to have 50k rows and 2 GB in size