Datasets:

inclusionAI
/

SWE-CARE

license: apache-2.0
task_categories:
  - text-generation
  - question-answering
language:
  - en
tags:
  - code
  - code-review
  - software-engineering
  - benchmark
  - python
size_categories:
  - n<1K
dataset_info:
  features:
    - name: instance_id
      dtype: string
    - name: repo
      dtype: string
    - name: language
      dtype: string
    - name: pull_number
      dtype: int64
    - name: title
      dtype: string
    - name: body
      dtype: string
    - name: created_at
      dtype: string
    - name: problem_statement
      dtype: string
    - name: hints_text
      dtype: string
    - name: resolved_issues
      list:
        - name: body
          dtype: string
        - name: number
          dtype: int64
        - name: title
          dtype: string
    - name: base_commit
      dtype: string
    - name: commit_to_review
      struct:
        - name: head_commit
          dtype: string
        - name: head_commit_message
          dtype: string
        - name: patch_to_review
          dtype: string
    - name: reference_review_comments
      list:
        - name: diff_hunk
          dtype: string
        - name: line
          dtype: int64
        - name: original_line
          dtype: int64
        - name: original_start_line
          dtype: int64
        - name: path
          dtype: string
        - name: start_line
          dtype: int64
        - name: text
          dtype: string
    - name: merged_commit
      dtype: string
    - name: merged_patch
      dtype: string
    - name: metadata
      struct:
        - name: difficulty
          dtype: string
        - name: estimated_review_effort
          dtype: int64
        - name: problem_domain
          dtype: string
  splits:
    - name: dev
      num_bytes: 341885132
      num_examples: 7086
    - name: test
      num_bytes: 35656314
      num_examples: 671
  download_size: 137206004
  dataset_size: 377541446
configs:
  - config_name: default
    data_files:
      - split: dev
        path: data/dev-*
      - split: test
        path: data/test-*

SWE-CARE: A Comprehensiveness-aware Benchmark for Code Review Evaluation

Dataset Description

SWE-CARE (Software Engineering - Comprehensive Analysis and Review Evaluation) is a comprehensiveness-aware benchmark for evaluating Large Language Models (LLMs) on repository-level code review tasks. The dataset features real-world code review scenarios from popular open-source Python and Java repositories, with comprehensive metadata and reference review comments.

Dataset Summary

Repository: inclusionAI/SWE-CARE
Paper: CodeFuse-CR-Bench: A Comprehensiveness-aware Benchmark for End-to-End Code Review Evaluation
Languages: Python
License: Apache 2.0
Splits:
- test: 671 instances (primary evaluation set)
- dev: 7,086 instances (development/training set)

Dataset Structure

Data Instances

Each instance in the dataset represents a code review task with the following structure:

{
    "instance_id": "voxel51__fiftyone-2353@02e9ba1",
    "repo": "voxel51/fiftyone",
    "language": "Python",
    "pull_number": 2353,
    "title": "Fix issue with dataset loading",
    "body": "This PR fixes...",
    "created_at": "2023-01-15T10:30:00Z",
    "problem_statement": "Issue #2350: Dataset fails to load...",
    "hints_text": "Comments from the issue discussion...",
    "resolved_issues": [
        {
            "number": 2350,
            "title": "Dataset loading error",
            "body": "When loading datasets..."
        }
    ],
    "base_commit": "abc123...",
    "commit_to_review": {
        "head_commit": "def456...",
        "head_commit_message": "Fix dataset loading logic",
        "patch_to_review": "diff --git a/file.py..."
    },
    "reference_review_comments": [
        {
            "text": "Consider adding error handling here",
            "path": "src/dataset.py",
            "diff_hunk": "@@ -10,5 +10,7 @@...",
            "line": 15,
            "start_line": 14,
            "original_line": 15,
            "original_start_line": 14
        }
    ],
    "merged_commit": "ghi789...",
    "merged_patch": "diff --git a/file.py...",
    "metadata": {
        "problem_domain": "Bug Fixes",
        "difficulty": "medium",
        "estimated_review_effort": 3
    }
}

Data Fields

Core Fields

instance_id (string): Unique identifier in format repo_owner__repo_name-PR_number@commit_sha_short
repo (string): GitHub repository in format owner/name
language (string): Primary programming language (Python or Java)
pull_number (int): GitHub pull request number
title (string): Pull request title
body (string): Pull request description
created_at (string): ISO 8601 timestamp of PR creation

Problem Context

problem_statement (string): Combined title and body of resolved issue(s)
hints_text (string): Relevant comments from issues prior to the PR
resolved_issues (list): Array of resolved issues with:
- number (int): Issue number
- title (string): Issue title
- body (string): Issue description

Code Changes

base_commit (string): Base commit SHA before changes
commit_to_review (dict): The commit being reviewed:
- head_commit (string): Commit SHA to review
- head_commit_message (string): Commit message
- patch_to_review (string): Git diff of changes to review
merged_commit (string): Final merged commit SHA
merged_patch (string): Final merged changes (ground truth)

Reference Reviews

reference_review_comments (list): Human code review comments with:
- text (string): Review comment text
- path (string): File path being reviewed
- diff_hunk (string): Relevant code diff context
- line (int): Line number in new version
- start_line (int): Start line for multi-line comments
- original_line (int): Line number in original version
- original_start_line (int): Original start line

Metadata

metadata (dict): LLM-classified attributes:
- problem_domain (string): Category like "Bug Fix", "Feature", "Refactoring", etc.
- difficulty (string): "Easy", "Medium", or "Hard"
- estimated_review_effort (int): Scale of 1-5 for review complexity

Data Splits

Split	Instances	Description
test	671	Primary evaluation set for benchmarking
dev	7,086	Development set for training/fine-tuning

Usage

Loading the Dataset

from datasets import load_dataset

# Load the test split (default for evaluation)
dataset = load_dataset("inclusionAI/SWE-CARE", split="test")

# Load the dev split
dev_dataset = load_dataset("inclusionAI/SWE-CARE", split="dev")

# Load both splits
full_dataset = load_dataset("inclusionAI/SWE-CARE")

Using with SWE-CARE Evaluation Framework

from swe_care.utils.load import load_code_review_dataset

# Load from Hugging Face (default)
instances = load_code_review_dataset()

# Access instance data
for instance in instances:
    print(f"Instance: {instance.instance_id}")
    print(f"Repository: {instance.repo}")
    print(f"Problem: {instance.problem_statement}")
    print(f"Patch to review: {instance.commit_to_review.patch_to_review}")
    print(f"Reference comments: {len(instance.reference_review_comments)}")

Running Evaluation

See the GitHub repository for detailed documentation and examples.

Evaluation Metrics and Baselines Results

See the paper for comprehensive evaluation metrics and baseline results on various LLMs.

Additional Information

Citation

If you use this dataset in your research, please cite:

@misc{guo2025codefusecrbenchcomprehensivenessawarebenchmarkendtoend,
      title={CodeFuse-CR-Bench: A Comprehensiveness-aware Benchmark for End-to-End Code Review Evaluation in Python Projects}, 
      author={Hanyang Guo and Xunjin Zheng and Zihan Liao and Hang Yu and Peng DI and Ziyin Zhang and Hong-Ning Dai},
      year={2025},
      eprint={2509.14856},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2509.14856}, 
}

Contributions

We welcome contributions! Please see our GitHub repository for:

Data collection improvements
New evaluation metrics
Baseline model results
Bug reports and feature requests

License

This dataset is released under the Apache 2.0 License. See LICENSE for details.

Changelog

v0.2.0 (2025-10): Expanded dataset to 671 test instances
v0.1.0 (2025-09): Initial release with 601 test instances and 7,086 dev instances