The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 91, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 193, in _generate_tables
examples = [ujson_loads(line) for line in batch.splitlines()]
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
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:
- AI Code Review Systems
- Test Case Generation Models
- 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:
- Pull request metadata
- Code patches
- Human review comments
- Anonymized contributor identifiers
- Repository license information
- Derived evaluation metadata
Example:
```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
- Multi-pass regex filtering
- NLP-based name detection
- Stable pseudonym mapping
- Patch-level sanitization
- 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 |
| 2 | django/django | BSD-3-Clause | https://github.com/django/django |
| 3 | tiangolo/fastapi | MIT | https://github.com/tiangolo/fastapi |
| 4 | numpy/numpy | BSD-3-Clause | https://github.com/numpy/numpy |
| 5 | pandas-dev/pandas | BSD-3-Clause | https://github.com/pandas-dev/pandas |
| 6 | scikit-learn/scikit-learn | BSD-3-Clause | https://github.com/scikit-learn/scikit-learn |
| 7 | pytorch/pytorch | BSD-3-Clause | https://github.com/pytorch/pytorch |
| 8 | tensorflow/tensorflow | Apache-2.0 | https://github.com/tensorflow/tensorflow |
| 9 | python/cpython | PSF License | 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
- Focuses on large, mature OSS projects
- Underrepresents small/private repos
- English-centric discussions
- 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
- Downloads last month
- 8