---
license: mit
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: eval
    path: data/eval-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: repo
    dtype: string
  - name: fix_commit
    dtype: string
  - name: buggy_commit
    dtype: string
  - name: message
    dtype: string
  - name: files
    list:
    - name: path
      dtype: string
    - name: patch
      dtype: string
    - name: additions
      dtype: int64
    - name: deletions
      dtype: int64
    - name: language
      dtype: string
  - name: timestamp
    dtype: timestamp[s]
  splits:
  - name: train
    num_bytes: 1449699066
    num_examples: 103096
  - name: eval
    num_bytes: 25215784
    num_examples: 3000
  - name: test
    num_bytes: 25215784
    num_examples: 3000
  download_size: 507195718
  dataset_size: 1500130634
task_categories:
- text-generation
language:
- en
tags:
- code
pretty_name: Github issues dataset
size_categories:
- 10K<n<100K
---

# GitHub Pull Request Bug–Fix Dataset

[Kaggle url](https://www.kaggle.com/datasets/adhavanyuvaraj/github-issues)

A **curated, high-signal dataset of real-world software bugs and fixes** collected from **25 popular open-source GitHub repositories**.  
Each entry corresponds to a **single pull request (PR)** and pairs contextual metadata with the **exact code changes (unified diffs)** that fixed the bug.

This dataset is designed for:

- **Automated program repair**
- **Bug-fix patch generation**
- **LLM-based code and debugging agents**
- **Empirical software engineering research**

---

## Data collection methodology

Data was collected using the **GitHub REST API** and post-processed into a structured format.

To maintain quality and usefulness:

- Only **merged pull requests** were included
- Each PR must represent a **bug fix or correctness change**
- Fixes are linked to both the **buggy commit** and the **fix commit**
- Code changes are stored as **unified diffs** at the file level
- Low-signal PRs (refactors, formatting-only changes, discussions) were filtered out
- PRs without meaningful code changes were excluded

Each dataset row represents **one bug–fix PR**.

---

## Dataset schema

Each entry follows the schema below:

```json
{
  "repo": "owner/repository",
  "pr_number": 12345,
  "title": "Short description of the fix",
  "body": "Pull request description and context",
  "buggy_commit": "abcdef123456...",
  "fix_commit": "fedcba654321...",
  "buggy_distance": 12,
  "confidence": "medium",
  "files": [
    {
      "filename": "path/to/file.ext",
      "patch": "unified diff representing the fix",
      "additions": 10,
      "deletions": 2
    }
  ]
}
```

| Field               | Description                                       |
| ------------------- | ------------------------------------------------- |
| `repo`              | GitHub repository containing the pull request     |
| `pr_number`         | Pull request number                               |
| `title`             | Pull request title                                |
| `body`              | Pull request description and discussion context   |
| `buggy_commit`      | Commit introducing or containing the bug          |
| `fix_commit`        | Commit that fixes the bug                         |
| `buggy_distance`    | Number of commits between buggy and fix commits   |
| `confidence`        | Heuristic confidence level of bug–fix correctness |
| `files`             | List of files modified by the fix                 |
| `files[].filename`  | Path to the modified file                         |
| `files[].patch`     | Unified diff containing the code changes          |
| `files[].additions` | Number of lines added                             |
| `files[].deletions` | Number of lines removed                           |

## 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 patches from real pull request context
* Studying bug-fix patterns across large codebases
* Building autonomous debugging or repair agents
* Research in program repair, code synthesis, and software maintenance

It is not intended for:

* Pull request classification or triage
* Sentiment analysis

## Limitations

The dataset reflects real-world noise from GitHub pull requests
Buggy commit identification is heuristic and may be imperfect
Some fixes involve refactoring or design changes rather than minimal patches
No guarantee that fixes represent optimal or best-practice solutions

> ⚠️ Warning
This dataset currently includes pull requests from 10 of the planned 25 repositories, totaling approximately 24,000 entries.
The final release is expected to contain ~50,000 entries and occupy ~2 GB of storage.