| title | emoji | colorFrom | colorTo | sdk | sdk_version | app_file | hf_oauth | pinned | short_description |
|---|---|---|---|---|---|---|---|---|---|
SWE-Model-Arena |
🎯 |
green |
red |
gradio |
5.50.0 |
app.py |
true |
false |
Chatbot arena for software engineering tasks |
Welcome to SWE-Model-Arena, an open-source platform designed for evaluating software engineering-focused foundation models (FMs), particularly large language models (LLMs). SWE-Model-Arena benchmarks models in iterative, context-rich workflows that are characteristic of software engineering (SE) tasks.
- Multi-Round Conversational Workflows: Evaluate models through extended, context-dependent interactions that mirror real-world SE processes.
- RepoChat Integration: Automatically inject repository context (issues, commits, PRs) into conversations for more realistic evaluations.
- Advanced Evaluation Metrics: Assess models using a comprehensive suite of metrics including:
- Traditional ranking metrics: Elo ratings and win rates to measure overall model performance
- Network-based metrics: Eigenvector centrality and PageRank to identify influential models in head-to-head comparisons
- Community detection metrics: Newman modularity to reveal clusters of models with similar capabilities
- Consistency metrics: Self-play match analysis to quantify model determinism and reliability
- Efficiency metrics: Conversation efficiency index to measure response quality relative to length
- Transparent, Open-Source Leaderboard: View real-time model rankings across diverse SE workflows with full transparency.
- Intelligent Request Filtering: Employ
gpt-oss-safeguard-20bas a guardrail to automatically filter out non-software-engineering-related requests, ensuring focused and relevant evaluations.
Existing evaluation frameworks (e.g. LMArena) often don't address the complex, iterative nature of SE tasks. SWE-Model-Arena fills critical gaps by:
- Supporting context-rich, multi-turn evaluations to capture iterative workflows
- Integrating repository-level context through RepoChat to simulate real-world development scenarios
- Providing multidimensional metrics for nuanced model comparisons
- Focusing on the full breadth of SE tasks beyond just code generation
- Submit a Prompt: Sign in and input your SE-related task (optional: include a repository URL for RepoChat context)
- Compare Responses: Two anonymous models provide responses to your query
- Continue the Conversation: Test contextual understanding over multiple rounds
- Vote: Choose the better model at any point, with ability to re-assess after multiple turns
- A Hugging Face account
- Navigate to the SWE-Model-Arena platform
- Sign in with your Hugging Face account
- Enter your SE task prompt (optionally include a repository URL for RepoChat)
- Engage in multi-round interactions and vote on model performance
We welcome contributions from the community! Here's how you can help:
- Submit SE Tasks: Share your real-world SE problems to enrich our evaluation dataset
- Report Issues: Found a bug or have a feature request? Open an issue in this repository
- Enhance the Codebase: Fork the repository, make your changes, and submit a pull request
Your interactions are anonymized and used solely for improving SWE-Model-Arena and FM benchmarking. By using SWE-Model-Arena, you agree to our Terms of Service.
- Analysis of Real-World SE Workloads: Identify common patterns and challenges in user-submitted tasks
- Multi-Round Evaluation Metrics: Develop specialized metrics for assessing model adaptation over successive turns
- Expanded FM Coverage: Include multimodal and domain-specific foundation models
- Advanced Context Compression: Integrate techniques like LongRope and SelfExtend to manage long-term memory in multi-round conversations
For inquiries or feedback, please open an issue in this repository. We welcome your contributions and suggestions!
Made with ❤️ for SWE-Model-Arena. If this work is useful to you, please consider citing our vision paper:
@inproceedings{zhao2025se,
title={SE Arena: An Interactive Platform for Evaluating Foundation Models in Software Engineering},
author={Zhao, Zhimin},
booktitle={2025 IEEE/ACM Second International Conference on AI Foundation Models and Software Engineering (Forge)},
pages={78--81},
year={2025},
organization={IEEE}
}