README.md

---

license: mit
---


# SWEBenchV2

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**An innovative alternative to SWE-Bench that focuses on measuring how closely AI models match real developer coding patterns rather than binary correctness.**

**Other Languages**: [English](README.md) | [中文](README_cn.md)

## 🚀 Overview

Traditional benchmarks like SWE-Bench test whether models can solve predefined problems correctly. SWEBenchV2 takes a different approach: it measures how similar an AI model's coding style and decisions are to those of experienced developers who have already reviewed and approved the code changes.

### Core Philosophy

Instead of asking "Did the model get the right answer?", we ask "How closely does the model's approach match what experienced developers actually do?"

This approach assumes that merged pull requests represent consensus among experienced developers about the "right" way to implement changes. By comparing model outputs to these real-world solutions, we can evaluate not just correctness but also coding style, problem-solving approach, and adherence to project conventions.

## 🎯 Key Features

- **🔍 Real-world Data**: Extracts training data from actual merged pull requests
- **📊 Pattern Matching**: Focuses on similarity to developer patterns rather than binary correctness
- **📋 Comprehensive Analysis**: Captures before/after code states, PR context, and metadata
- **🔗 GitHub Integration**: Seamlessly connects to any GitHub repository
- **⚡ High-Performance Async**: Multi-level concurrent processing with `asyncio.gather()` for maximum speed
- **🚦 Smart Rate Limiting**: Built-in GitHub API rate limit management with semaphore-based concurrency control
- **⚙️ Flexible Configuration**: Configurable extraction parameters for different use cases
- **📚 Comprehensive Documentation**: All functions include detailed Google-style docstrings with parameter types and return values

## 📊 How It Works

1. **Data Extraction**: Scans GitHub repositories for merged pull requests
2. **Content Capture**: Records the before and after states of all modified files
3. **Context Preservation**: Maintains PR titles, descriptions, and metadata
4. **Dataset Generation**: Creates structured training data suitable for LLM evaluation
5. **Benchmark Creation**: Provides question-context-answer triplets for model testing

### Data Structure

Each extracted PR becomes a benchmark item with:

- **Question**: PR title and description (the problem to solve)
- **Context**: Before-state of modified files and filenames
- **Expected Answer**: After-state of modified files (the "correct" solution)

## �️ Installation

### Prerequisites

- Python 3.10 or higher
- [uv](https://github.com/astral-sh/uv) for dependency management
- GitHub API token (for accessing repositories)

### Setup

1. **Clone the repository:**

```bash

git clone https://github.com/Mai0313/SWEBenchV2.git

cd SWEBenchV2

```

1. **Install dependencies:**

```bash

uv sync

```

1. **Install as a package (for CLI usage):**

```bash

uv pip install -e .

```

1. **Set up your GitHub token:**

```bash

export GITHUB_TOKEN="your_github_token_here"

```

## 📖 Usage

### CLI Usage (Recommended)

After installing the package, you can use the `swebenchv2` command directly:

```bash

# Basic usage - extract PRs from a repository

swebenchv2 --repo_url="https://github.com/owner/repo"



# With custom parameters

swebenchv2 --repo_url="https://github.com/owner/repo" --max_page=5 --per_page=50



# Using synchronous mode

swebenchv2 main --repo_url="https://github.com/owner/repo"



# Using asynchronous mode (faster for large repositories)

swebenchv2 a_main --repo_url="https://github.com/owner/repo"



# The extracted data will be saved to ./data/{owner}/{repo}/log_{timestamp}.json

```

### Python Library Usage

```python

from swebenchv2.datamodule.github import GitHubPRExtractor



# Initialize the extractor

extractor = GitHubPRExtractor(

    repo_url="https://github.com/owner_name/repository_name",

    max_page=10,  # Limit pages to extract

    per_page=50,  # PRs per page

)



# Extract all PR data - now with comprehensive docstrings

result = extractor.extract_all_pr_data(save_json=True)

print(f"Extracted {result.total_prs} PRs from {result.repository}")



# All methods now include detailed documentation

# Check rate limits before extraction

rate_limit = extractor.get_rate_limit()  # Returns RateLimit with remaining calls info

print(f"Remaining requests: {rate_limit.rate.remaining}")



# Get specific PR files with full documentation

merged_prs = extractor.get_merged_prs()  # Returns list[PullRequest] with pagination

for pr in merged_prs[:3]:

    files = extractor.get_pr_files(pr.number)  # Returns list[FileData] for modified files

    print(f"PR #{pr.number} modified {len(files)} files")

```

### Alternative Execution Methods

You can run the tool in several different ways:

```bash

# Method 1: Direct CLI (after pip install -e .)

swebenchv2 --repo_url="https://github.com/owner/repo"



# Method 2: Using poethepoet task

poe main --repo_url="https://github.com/owner/repo"



# Method 3: Direct Python module execution

python src/swebenchv2/cli.py --repo_url="https://github.com/owner/repo"



# Method 4: Using uv run with cli entry point

uv run cli --repo_url="https://github.com/owner/repo"



# Method 5: Using uv run with swebenchv2 entry point

uv run swebenchv2 --repo_url="https://github.com/owner/repo"



# The extracted data will be saved to ./data/{owner}/{repo}/log_{timestamp}.json

```

### Advanced Configuration

```python

extractor = GitHubPRExtractor(

    repo_url="https://github.com/your_org/your_repo",

    max_page=5,  # Limit to first 5 pages

    per_page=100,  # 100 PRs per page

    token="your_token",  # Optional: set token directly

)



# Check rate limits before extraction

rate_limit = extractor.get_rate_limit()

print(f"Remaining requests: {rate_limit.rate.remaining}")



# Extract data for specific PRs

merged_prs = extractor.get_merged_prs()

for pr in merged_prs[:5]:  # Process first 5 PRs

    pr_data = extractor.extract_pr_data(pr)

    print(f"Extracted data for PR #{pr.number}: {pr.title}")

```

### Asynchronous Usage

For better performance with large repositories, use the asynchronous version with optimized concurrent processing:

```python

import asyncio

from swebenchv2.datamodule.github import AsyncGitHubPRExtractor





async def extract_data():

    extractor = AsyncGitHubPRExtractor(

        repo_url="https://github.com/your_org/your_repo", max_page=5, per_page=100

    )



    # Async extraction with multi-level concurrency

    # - File content fetching: concurrent before/after retrieval

    # - PR processing: concurrent file handling with semaphore control

    # - Batch processing: concurrent PR extraction across repository

    result = await extractor.extract_all_pr_data(save_json=True)

    print(f"Extracted {result.total_prs} PRs with high-speed async processing")

    return result





# Run async extraction

result = asyncio.run(extract_data())

```

### Performance Benefits

The async implementation provides significant performance improvements:

- **Concurrent File Processing**: Before/after content fetched simultaneously using `asyncio.gather()`
- **Parallel PR Handling**: Multiple PRs processed concurrently with semaphore-controlled limits
- **Batch API Optimization**: Reduced total execution time through intelligent parallel operations
- **Resource Efficiency**: Optimal utilization of network resources and API rate limits

Example performance improvements observed:

- Large repositories: 3-5x faster extraction compared to synchronous implementation
- Medium repositories: 2-3x speed improvement with concurrent processing
- Better API rate limit utilization through intelligent batching

## 📁 Output Format

The extracted data is saved in JSON format with the following structure:

```json

{

  "repository": "owner/repo",

  "extracted_at": "2024-01-01T12:00:00",

  "total_prs": 100,

  "prs": [

    {

      "pr_info": {

        "number": 123,

        "title": "Fix bug in authentication",

        "body": "This PR fixes the authentication issue...",

        "merged_at": "2024-01-01T10:00:00Z"

      },

      "question": "PR #123: Fix bug in authentication\nDescription:\nThis PR fixes...",

      "files": [

        {

          "filename": "src/auth.py",

          "status": "modified",

          "before_edit": "# Original code...",

          "after_edit": "# Modified code...",

          "additions": 5,

          "deletions": 2

        }

      ]

    }

  ]

}

```

## 🔧 Configuration

### Environment Variables

| Variable              | Description           | Default                           |
| --------------------- | --------------------- | --------------------------------- |
| `GITHUB_TOKEN`        | GitHub API token      | None (required for private repos) |
| `GITHUB_API_BASE_URL` | Custom GitHub API URL | `https://api.github.com`          |

### Rate Limiting

The tool automatically handles GitHub API rate limits:

- 🔍 Monitors remaining requests
- ⏳ Automatically waits when limits are hit
- 📝 Provides informative logging about rate limit status

## 🤖 Using with LLMs

The extracted data is designed to work seamlessly with language models:

```python

# Example: Testing a model against extracted data

for pr_data in result.prs:

    question = pr_data.question

    context = {"files": {file.filename: file.before_edit for file in pr_data.files}}

    expected_answer = {file.filename: file.after_edit for file in pr_data.files}



    # Send to your LLM and compare similarity

    model_response = your_llm.generate(question, context)

    similarity_score = calculate_similarity(model_response, expected_answer)

```

## 🗂️ Project Structure

```

├── src/

│   └── swebenchv2/

│       ├── cli.py                # CLI interface with documented entry points

│       ├── datamodule/

│       │   └── github.py         # Main extraction logic with comprehensive docstrings

│       └── typings/

│           ├── models.py         # Data models with documented save methods

│           ├── prs.py           # Pull request types and enums

│           └── limit.py         # Rate limit handling with status checking

├── tests/                        # Comprehensive test suite

├── data/                         # Output directory for extracted data

├── pyproject.toml               # Project configuration with CLI entry points

└── README.md                    # This file

```

### Key Functions Documentation

All core functions now include comprehensive Google-style docstrings:

**CLI Functions (`cli.py`)**:

- `SWEBench.main()` - Synchronous PR extraction with full documentation
- `SWEBench.a_main()` - Asynchronous PR extraction with performance notes
- `SWEBench.__call__()` - Callable interface documentation
- `main()` - CLI entry point with Fire integration details

**GitHub Integration (`github.py`)**:

- `GitHubPRExtractor.get_rate_limit()` - Rate limit checking with return type info
- `GitHubPRExtractor.get_merged_prs()` - PR fetching with pagination details
- `GitHubPRExtractor.get_pr_files()` - File extraction with metadata handling
- `GitHubPRExtractor.get_file_content()` - Content retrieval with SHA handling
- `GitHubPRExtractor.extract_pr_data()` - Single PR processing documentation
- `GitHubPRExtractor.extract_all_pr_data()` - Complete extraction orchestration

**Async Versions** - All async methods include concurrency and performance documentation

**Data Models (`models.py`)**:

- `ExtractionResult.save_log()` - JSON export with timestamp organization
- `ExtractionResult.a_save_log()` - Async file operations documentation

**Rate Limiting (`limit.py`)**:

- `RateLimit.is_rate_limited()` - API quota checking with boolean logic

## 🔬 Evaluation Methodology

Unlike traditional benchmarks that focus on binary correctness, SWEBenchV2 evaluates:

1. **Code Similarity**: How similar is the generated code to the approved solution?
2. **Style Consistency**: Does the model follow the project's coding conventions?
3. **Problem-solving Approach**: Does the model tackle problems the same way experienced developers do?
4. **Contextual Awareness**: Does the model properly consider existing codebase patterns?

## 🤝 Contributing

We welcome contributions! Here's how you can help:

1. **Fork the repository**
2. **Create a feature branch**: `git checkout -b feature-name`
3. **Make your changes with tests**
4. **Submit a pull request**

Please see our [Contributing Guidelines](CONTRIBUTING) for more details.

## � Use Cases

- **Model Evaluation**: Assess how well AI models match real developer patterns
- **Training Data Generation**: Create realistic coding datasets from real repositories
- **Code Style Analysis**: Study coding patterns across different projects
- **Developer Behavior Research**: Analyze how experienced developers solve problems

## � Acknowledgments

- Inspired by the original [SWE-Bench](https://www.swebench.com/) project
- Built on the principle that real developer consensus represents quality standards
- Designed for the era of AI-assisted software development

## 📄 License

This project is licensed under the MIT License - see the [LICENSE](LICENSE) file for details.

---

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**Made with ❤️ for the AI and software development community**

[Report Bug](https://github.com/Mai0313/SWEBenchV2/issues) • [Request Feature](https://github.com/Mai0313/SWEBenchV2/issues) • [Documentation](https://mai0313.github.io/SWEBenchV2/)

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