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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 |