---
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: issue_number
    dtype: int64
  - name: issue_title
    dtype: string
  - name: issue_body
    dtype: string
  - name: commit_sha
    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: 112658160
    num_examples: 4000
  - name: split2
    num_bytes: 116854026
    num_examples: 4000
  - name: split3
    num_bytes: 115010499
    num_examples: 4000
  - name: split4
    num_bytes: 77644470
    num_examples: 2402
  download_size: 137319300
  dataset_size: 422167155
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:

```json
{
  "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

> **<span style="color:red;font-size:1.25rem">Warning</span>**: 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