README.md

metadata

license: other
task_categories:
  - text-generation
language:
  - en
  - code
tags:
  - code-review
  - code-generation
  - software-engineering
  - pull-requests
  - github
size_categories:
  - 100K<n<1M

Code Review Dataset

A large-scale dataset of the best human-written code reviews from top GitHub repositories.

Each row captures a moment where a human code reviewer left an inline comment on a pull request, and the author subsequently modified the code in response.

The dataset also includes negative examples — code from the same PRs that passed review without comments — to help models learn when code is acceptable.

This provides a natural signal for training models to:

• Generate code review comments given a code diff
• Apply review feedback by modifying code based on reviewer suggestions
• Understand code quality patterns across languages and projects
• Know when not to comment — recognizing clean code that needs no changes

Key Features

• 167K+ positive triplets from 725 top GitHub repositories
• 51K+ negative examples (~23% of dataset) of clean code labeled "No issues found."
• 37 programming languages (Python, TypeScript, Go, Rust, C++, JavaScript, C#, Java, Kotlin, Swift, and more)
• Human-only reviews: AI/bot reviewers (Copilot, linter bots, etc.) are excluded
• Quality-filtered: noise and auto-generated content removed
• Chunk-focused: ~50 lines of context around the reviewed code, not entire files
• Permissive licenses only: all source repos use MIT, Apache-2.0, BSD, or similar licenses
• Verified changes: only includes triplets where the code chunk actually changed after the review

Collection Methodology

1. Repo selection: Top GitHub repos by stars with permissive licenses, sourced from ronantakizawa/github-top-projects and curated additions
2. PR discovery: Paginate merged PRs, filter bot authors, fetch inline review comments
3. Comment filtering: Remove bots, noise patterns, auto-generated comments, non-English text, non-code files, reply comments
4. Triplet extraction: Fetch file contents at the review commit (before) and PR head (after), extract focused chunks around the comment line
5. Change verification: Only keep triplets where the code chunk around the comment actually changed
6. Negative extraction: For each reviewed PR, identify source code files that were changed but received no review comments; extract a ~50-line chunk as a negative example labeled "No issues found."

Splits

Split	Percentage	Description
train	90%	Training data
test	5%	Test data
validation	5%	Validation data

Splits are deterministic by repository — all examples from the same repo appear in the same split.

Schema

Column	Type	Description
pr_title	string	Pull request title
pr_number	int	PR number
repo_name	string	Full repo name (owner/repo)
repo_stars	int	GitHub stars
repo_language	string	Primary repo language
author_username	string	PR author's GitHub username
reviewer_username	string	Reviewer's GitHub username
before_code	string	~50 lines of code around the comment, before the fix
reviewer_comment	string	The inline review comment text (or "No issues found." for negatives)
after_code	string	~50 lines of code around the comment, after the fix
diff_context	string	The PR diff hunk where the comment was placed
file_path	string	File path within the repo
comment_line	int	Line number within the code chunk (0 for negatives)
language	string	Programming language
quality_score	float	Comment quality score (0.0-1.0; 1.0 for negatives)
comment_type	string	Category: suggestion, question, nitpick, bug, refactor, style, security, performance, none
comment_length	int	Character count of reviewer comment
before_lines	int	Line count of before code
after_lines	int	Line count of after code
is_negative	bool	True if this is a negative example (no reviewer comment)

Usage

from datasets import load_dataset

ds = load_dataset("ronantakizawa/github-codereview")

# Get a training example
example = ds["train"][0]
print(f"Review comment: {example['reviewer_comment']}")
print(f"Language: {example['language']}")
print(f"Before:\n{example['before_code'][:200]}")
print(f"After:\n{example['after_code'][:200]}")

Filter by language

python_reviews = ds["train"].filter(lambda x: x["language"] == "Python")

Filter by quality

high_quality = ds["train"].filter(lambda x: x["quality_score"] >= 0.5)

Positive examples only

positives = ds["train"].filter(lambda x: not x["is_negative"])

Negative examples only

negatives = ds["train"].filter(lambda x: x["is_negative"])

Citation

If you use this dataset, please cite:

@dataset{takizawa2026codereviewdiffs,
  title={Code Review Diffs: A Large-Scale Dataset of Review-Driven Code Changes},
  author={Takizawa, Ronan},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/ronantakizawa/github-codereview}
}