A brand-new, continuously updated SWE-bench-like dataset powered by an automated curation pipeline.

---

Note

The evaluation code in this repo is forked from SWE-bench/SWE-bench, with only minimal modifications to support evaluation on the SWE-bench-Live dataset. All other settings remain consistent with SWE-bench to reduce the migration effort. For code part, please respect the original license from the SWE-bench repository.

SWE-bench-Live is a live benchmark for issue resolving, designed to evaluate an AI system's ability to complete real-world software engineering tasks. Thanks to our automated dataset curation pipeline, we plan to update SWE-bench-Live on a monthly basis to provide the community with up-to-date task instances and support rigorous and contamination-free evaluation.

News

• 07/19/2025: We've employed a LLM filter to automatically filter full dataset to create SWE-bench-Live-Verified. The initial Verified subset contains 500 instances from 2024-07 to 2025-04.
• 06/30/2025: We’ve updated the dataset — it now includes a total of 1,565 task instances across 164 repositories!
• 05/21/2025: The initial release of SWE-bench-Live includes 1,319 latest (created after 2024) task instances, each paired with an instance-level Docker image for test execution, covering 93 repositories.

📁 Repository Structure

├── swebench/             # Core evaluation code (a fork of SWE-bench)
├── launch/               # RepoLaunch tool for environment setup
├── curation/             # Curation pipeline (scripts)
├── assets/               # Repo assets
├── ...
└── README.md             # This file

🚀 Set Up

# Python >= 3.10
pip install -e .

Test your installation by running:

python -m swebench.harness.run_evaluation \
    --dataset_name SWE-bench-Live/SWE-bench-Live \
    --split lite \
    --instance_ids amoffat__sh-744 \
    --namespace starryzhang \
    --predictions_path gold \
    --max_workers 1 \
    --run_id validate-gold

🚥 Evaluation

Evaluate your model on SWE-bench-Live.

python -m swebench.harness.run_evaluation \
    --dataset_name SWE-bench-Live/SWE-bench-Live \
    --split <lite/full> \
    --namespace starryzhang \
    --predictions_path <path_to_your_preds or gold> \
    --max_workers <num_workers> \
    --run_id <run_id>

Instance-level Docker images are hosted on DockerHub.

🐳 Dataset Curation

In SWE-bench-Live, we propose an automated pipeline for curating SWE-bench-like dataset.

SWE-bench-Live Curation Pipeline

RepoLaunch

We addresses the bottleneck of setting up execution environments by automating the process through an LLM-based agentic tool – RepoLaunch. It can deliver a testable containerized environment for any given GitHub repository, thereby enabling test-based evaluation in SWE-bench-Live.

See ./launch folder for RepoLaunch code.

Note

We provide a tutorial to help you walk through the entire dataset curation process, starting from repository crawling.

⬆️ Submit your results

Thank you for your interest in submitting results to SWE-bench-Live! We coordinate results submission via Pull Requests, see SWE-bench-Live/submissions for instructions.

🙏 Acknowledgements

SWE-bench-Live is built upon the foundation of SWE-bench. We extend our gratitude to the original SWE-bench team for their pioneering work in software engineering evaluation benchmarks.

📚 Citation

If you found the SWE-bench-Live and SWE-bench helpful for your research, please cite as follows

@article{zhang2025swebenchgoeslive,
  title={SWE-bench Goes Live!},
  author={Linghao Zhang and Shilin He and Chaoyun Zhang and Yu Kang and Bowen Li and Chengxing Xie and Junhao Wang and Maoquan Wang and Yufan Huang and Shengyu Fu and Elsie Nallipogu and Qingwei Lin and Yingnong Dang and Saravan Rajmohan and Dongmei Zhang},
  journal={arXiv preprint arXiv:2505.23419},
  year={2025}
}

@inproceedings{jimenez2024swebench,
    title={SWE-bench: Can Language Models Resolve Real-world Github Issues?},
    author={Carlos E Jimenez and John Yang and Alexander Wettig and Shunyu Yao and Kexin Pei and Ofir Press and Karthik R Narasimhan},
    booktitle={The Twelfth International Conference on Learning Representations},
    year={2024},
    url={https://openreview.net/forum?id=VTF8yNQM66}
}

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

When you submit a pull request, a CLA bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., status check, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.