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Datasets:
ronantakizawa
/
github-codereview

Tasks:
Text Generation
Modalities:
Tabular
Text
Formats:
parquet
Languages:
English
code
Size:
100K - 1M
Tags:
code-review
code-generation
software-engineering
pull-requests
github
Libraries:
Datasets
Dask
Polars
License:
Dataset card Data Studio Files
xet
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metadata 
license: mit
task_categories:
  - text-generation
language:
  - en
  - code
tags:
  - code-review
  - code-generation
  - software-engineering
  - pull-requests
  - github
size_categories:
  - 100K<n<1M

Code Review Diffs

A large-scale dataset of (before_code, reviewer_comment, after_code) triplets extracted from merged pull requests on top GitHub repositories.

Dataset Description

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. 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

Key Features

  • 118K+ triplets from 562 top GitHub repositories
  • 20+ programming languages (Python, TypeScript, Go, C++, Rust, JavaScript, C#, Java, Kotlin, Swift, and more)
  • Quality-filtered: bot comments, noise ("LGTM", "+1"), 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

Schema

Column Type Description
before_code string ~50 lines of code around the comment, before the fix
reviewer_comment string The inline review comment text
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 where the comment was placed
language string Programming language
quality_score float Comment quality score (0.0-1.0)
comment_length int Character count of reviewer comment
before_lines int Line count of before code
after_lines int Line count of after code
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
reviewer_username string Reviewer's GitHub username
author_username string PR author's GitHub username

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"] >= 0.3)

Collection Methodology

  1. Repo selection: Top 10,000 GitHub repos by stars with permissive licenses from ronantakizawa/github-top-projects
  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

Quality Filters Applied

  • Bot authors excluded (dependabot, renovate, codecov, etc.)
  • Comments < 30 characters excluded
  • Noise patterns excluded (LGTM, +1, emoji-only, etc.)
  • Auto-generated comments excluded (coverage reports, CI output)
  • Non-English comments excluded (ASCII ratio < 70%)
  • Non-source-code files excluded
  • Reply comments excluded (only top-level review comments)
  • Files > 512 KB excluded
  • PRs with < 10 or > 500 changed lines excluded

Splits

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

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

Limitations

  • Review comments may address style/formatting rather than substantive logic changes
  • The "after" code may include changes unrelated to the specific review comment
  • Line number alignment may be imprecise when multiple commits occur between review and merge
  • Some original_commit_id SHAs may be unavailable due to force-pushes; in these cases, the PR merge base is used as the "before" state

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}
}