| --- |
| 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 |
