Datasets:

ronantakizawa
/

codereview-bench

license: mit
task_categories:
  - text-generation
language:
  - en
  - code
tags:
  - code-review
  - benchmark
  - code-generation
  - software-engineering
size_categories:
  - 10K<n<100K
configs:
  - config_name: code-editing
    data_files:
      - split: train
        path: data/code-editing/train/*.parquet
      - split: test
        path: data/code-editing/test/*.parquet
      - split: validation
        path: data/code-editing/validation/*.parquet
  - config_name: comment-generation
    data_files:
      - split: train
        path: data/comment-generation/train/*.parquet
      - split: test
        path: data/comment-generation/test/*.parquet
      - split: validation
        path: data/comment-generation/validation/*.parquet
  - config_name: default
    data_files:
      - split: train
        path: data/code-editing/train/*.parquet
      - split: test
        path: data/code-editing/test/*.parquet
      - split: validation
        path: data/code-editing/validation/*.parquet

CodeReview-Bench

A benchmark for evaluating models on two code review tasks, curated from ronantakizawa/github-codereview.

Tasks

1. Code Editing

Given code and a reviewer comment, apply the requested change.

Input: before_code, reviewer_comment, language, diff_context
Target: after_code

from datasets import load_dataset

ds = load_dataset("ronantakizawa/codereview-bench", "code-editing")
example = ds["test"][0]

prompt = f"""Apply the following review comment to the code.

Review: {example['reviewer_comment']}

Code:
{example['before_code']}

Updated code:"""

2. Comment Generation

Given a code diff, generate the review comment a human reviewer would write.

Input: before_code, after_code, diff_context, language
Target: reviewer_comment

ds = load_dataset("ronantakizawa/codereview-bench", "comment-generation")
example = ds["test"][0]

prompt = f"""Review the following code change and provide feedback.

Before:
{example['before_code']}

After:
{example['after_code']}

Review comment:"""

Filtering Criteria

This benchmark is a quality-filtered subset of the full dataset:

Filter	Threshold
Positive examples only	`is_negative = False`
Quality score	>= 0.5
Comment length	>= 50 characters
Code context	>= 10 lines (before and after)
Comment types	bug, security, performance, refactor, suggestion

Excluded: nitpick, style, question, and negative examples.

Schema

Code Editing

Column	Type	Role
`before_code`	string	Input
`reviewer_comment`	string	Input
`language`	string	Input
`diff_context`	string	Input
`after_code`	string	Target
`repo_name`	string	Metadata
`file_path`	string	Metadata
`comment_type`	string	Metadata
`quality_score`	float	Metadata

Comment Generation

Column	Type	Role
`before_code`	string	Input
`after_code`	string	Input
`diff_context`	string	Input
`language`	string	Input
`reviewer_comment`	string	Target
`repo_name`	string	Metadata
`file_path`	string	Metadata
`comment_type`	string	Metadata
`quality_score`	float	Metadata

Evaluation

Code Editing

CodeBLEU: Measures structural and syntactic similarity of generated code
Exact match: Percentage of outputs matching the target exactly
Edit similarity: Normalized edit distance between generated and target code

Comment Generation

BERTScore: Semantic similarity between generated and reference comments
ROUGE-L: Longest common subsequence overlap
Human evaluation: Recommended for final assessment — automated metrics correlate poorly with review quality

Splits

Split	Description
train	Training data (90%)
test	Held-out evaluation (5%)
validation	Development/tuning (5%)

Splits are repo-deterministic — no repo appears in multiple splits.

Citation

@dataset{takizawa2026codereviewbench,
  title={CodeReview-Bench: A Benchmark for Review-Driven Code Changes},
  author={Takizawa, Ronan},
  year={2026},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/ronantakizawa/codereview-bench}
}