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README.md

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+# 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
+
+
 ---
 license: mit
 tags: