---
license: mit
tags:
- ai
- modeltraining
- machinelearning
language:
- en
pretty_name: 'AI code review benchmark dataset '
---

# PR_Review-Benchmark-Dataset
A High-Signal Dataset for Evaluating AI Code Review & Test Generation Systems

## Overview
PR_Review-Benchmark-Dataset is a curated dataset of merged GitHub Pull Requests, code patches, and human review comments, designed for benchmarking:

 1. AI Code Review Systems
2. Test Case Generation Models
3. Software Engineering LLMs

All data is derived from publicly available open-source repositories under permissive licenses and processed using a privacy-preserving pipeline.
This dataset is suitable for academic research, open benchmarking, and commercial evaluation.

## Dataset Contents
Each entry contains:
1. Pull request metadata
2. Code patches
3. Human review comments
4. Anonymized contributor identifiers
5. Repository license information
6. Derived evaluation metadata
   
### Example:
```md
```json
{
  "_id": "...",
  "repo": "psf/requests",
  "license_spdx": "Apache-2.0",
  "title": "...",
  "files": [...],
  "reviews": [...],
  "review_comments": [...],
  "meta": {...}
}
```


### Schema Overview
| Field                           | Description                   |
| ------------------------------- | ----------------------------- |
| `_id`                           | Unique salted hash identifier |
| `_schema_version`               | Dataset schema version        |
| `_cleaned_at`                   | Processing timestamp          |
| `repo`                          | Repository name               |
| `license_spdx`                  | SPDX license identifier       |
| `files`                         | Modified files with patches   |
| `reviews`                       | Pull request reviews          |
| `review_comments`               | Inline reviewer comments      |
| `meta.touches_tests`            | Test-modifying PR flag        |
| `meta.meaningful_comment_count` | Review signal strength        |
| `meta.review_state_summary`     | Review status summary         |

## Legal & Privacy Compliance
This dataset follows a Privacy by Design approach and complies with applicable data protection regulations.
🇮🇳 India: Digital Personal Data Protection Act (DPDP), 2023
Data is processed under Section 3(c)(ii) exemption:
       Publicly available personal data made available by the data principal.
       
#### Privacy Measures
| Measure           | Description                                       |
| ----------------- | ------------------------------------------------- |
| Anonymization     | Usernames are replaced with salted SHA-256 hashes |
| Redaction         | Names, emails, URLs, and PII are removed          |
| Content Filtering | Private/non-public repos excluded                 |
| License Filtering | Only permissive licenses allowed                  |

#### Data Processing Pipeline
1. Multi-pass regex filtering
2. NLP-based name detection
3. Stable pseudonym mapping
4. Patch-level sanitization
5. Deduplication
No original personal identifiers are retained.

## Attribution & Provenance

### Dataset Attribution Manifest

Generated on: 2026-02-12
This dataset contains code snippets, pull request metadata, and human review comments derived from the following public open-source repositories.
All data was collected from repositories explicitly using permissive licenses.

#### Repositories Included
| # | Repository                | License      | URL                                                                                          |
| - | ------------------------- | ------------ | -------------------------------------------------------------------------------------------- |
| 1 | psf/requests              | Apache-2.0   | [https://github.com/psf/requests](https://github.com/psf/requests)                           |
| 2 | django/django             | BSD-3-Clause | [https://github.com/django/django](https://github.com/django/django)                         |
| 3 | tiangolo/fastapi          | MIT          | [https://github.com/tiangolo/fastapi](https://github.com/tiangolo/fastapi)                   |
| 4 | numpy/numpy               | BSD-3-Clause | [https://github.com/numpy/numpy](https://github.com/numpy/numpy)                             |
| 5 | pandas-dev/pandas         | BSD-3-Clause | [https://github.com/pandas-dev/pandas](https://github.com/pandas-dev/pandas)                 |
| 6 | scikit-learn/scikit-learn | BSD-3-Clause | [https://github.com/scikit-learn/scikit-learn](https://github.com/scikit-learn/scikit-learn) |
| 7 | pytorch/pytorch           | BSD-3-Clause | [https://github.com/pytorch/pytorch](https://github.com/pytorch/pytorch)                     |
| 8 | tensorflow/tensorflow     | Apache-2.0   | [https://github.com/tensorflow/tensorflow](https://github.com/tensorflow/tensorflow)         |
| 9 | python/cpython            | PSF License  | [https://github.com/python/cpython](https://github.com/python/cpython)                       |
### Legal Notice
All original code is copyright (c) its respective contributors.
This dataset is a derived work for machine learning research and benchmarking.
No ownership of original content is claimed.

## Limitations & Biases

1. Focuses on large, mature OSS projects
2. Underrepresents small/private repos
3. English-centric discussions
4. Limited to merged PRs

This dataset reflects real-world OSS workflows, not all software development contexts.

## Recommended Citation
* PR Review Benchmark Dataset (2026).
* Curated for Code-LLM Evaluation.

## Responsible Use
#### This dataset is intended for:
* Improving developer tools
* Advancing ML research
* Supporting open-source ecosystems
#### Users must not:
* Attempt deanonymization
* Extract personal identities
* Misrepresent authorship
* Violate upstream licenses

## Contact & Maintenance
For issues, improvements, or corrections:
* Open a GitHub Issue
* Submit a Pull Request