README.md

metadata

language:
  - en
license: apache-2.0
size_categories:
  - 1K<n<10K
task_categories:
  - text-generation
pretty_name: MEnvData-SWE
tags:
  - code
  - software-engineering
  - docker
  - environment
  - multi-language
  - swe-bench

MEnvData-SWE: Polyglot Software Engineering Dataset with Executable Environments

📋 Dataset Description

MEnvData-SWE is the largest open-source polyglot dataset of realistic verifiable Docker environments, comprising 3,005 task instances from 942 repositories across 10 programming languages. Each instance includes a fully executable Docker environment with pre-verified test cases, environment setup scripts, and evaluation scripts.

Key Features

• 🌐 Multi-Language Support: 10 programming languages (Python, Java, TypeScript, JavaScript, Rust, Go, C++, Ruby, PHP, C)
• 🐳 3,005 Docker Images: Pre-built environment images with verified dependencies
• ✅ Verified Quality: Each instance includes Fail2Pass validated test cases
• 🔧 Complete Setup Scripts: Both incremental and full environment configuration scripts
• 📝 Evaluation Scripts: Ready-to-use verification scripts with exit code validation
• ⚡ Efficient Verification: Exit code-based validation (no complex log parsing required)

Dataset

Name	Value
Total Instances	3,005
Repositories	942
Languages	10
Docker Images	3,005 (one per instance)

🐳 Docker Image Registry

All pre-built Docker images are publicly available on Docker Hub:

🔗 Docker Hub Repository: https://hub.docker.com/u/mcatwj

Each instance's image_name field references a specific image that can be pulled directly:

# Example: Pull a specific instance image
docker pull mcatwj/swe-images-c:systemd-systemd-pr-24645

# Or use the image_name from the dataset
docker pull mcatwj/<image_name_from_dataset>

All images are fully configured with:

• Project dependencies installed
• Repository cloned at /testbed
• Ready for immediate test execution

📊 Dataset Structure

MEnvData-SWE extends the standard SWE-Bench schema with executable environment configurations. Each instance contains the following fields:

Field	Type	Description
repo	str	The full GitHub repository name (e.g., "home-assistant/core").
pull_number	int	The pull request number associated with the fix.
instance_id	str	A unique identifier for the task instance.
issue_numbers	list	A list of linked issue numbers.
base_commit	str	The commit SHA of the repository prior to the fix.
version	str	The version of the dataset.
patch	str	The ground-truth patch (git diff) that resolves the issue.
test_patch	str	The test patch (git diff) containing new tests to reproduce the issue.
problem_statement	str	The natural language description of the issue.
hints_text	str	Hints extracted from the issue discussion.
all_hints_text	str	Comprehensive context including all comments and reviews.
commit_urls	list	A list of URLs pointing to the relevant commits.
created_at	str	The creation timestamp (e.g., "2015-12-27T19:33:55Z").
language	str	The programming language (e.g., "Python").
env_setup_script	str	🆕 Incremental bash commands used to configure the environment (for reuse scenarios).
original_env_setup_script	str	🆕 The foundational setup commands. Represents the reused base image's setup or the full build script if built from scratch.
eval_script	str	🆕 The complete verification script that applies the test_patch and executes the test commands.
image_name	str	🆕 The specific Docker image name/tag available for this instance.

🚀 Usage

Loading the Dataset

from datasets import load_dataset

# Load the full dataset
dataset = load_dataset("ernie-research/MEnvData-SWE")

# Access a single instance
instance = dataset['train'][0]
print(f"Repository: {instance['repo']}")
print(f"Language: {instance['language']}")
print(f"Docker Image: {instance['image_name']}")

Example Instance

{
    "repo": "systemd/systeinstance_id": "systemd__systemd-24645",
    "language": "C",
    "image_name": "swe-images-c:systemd-systemd-pr-24645",
    "base_commit": "6d64cb0625691e2b9eda8babe07ac8281f9467ee",
    "env_setup_script": "#!/usr/bin/env bash\nset -e\ncd /testbed\ngit reset --hard 6d64cb0625...",
    "eval_script": "#!/usr/bin/env bash\nset -uxo pipefail\nexport LANG=en_US.UTF-8...",
    ...
}

🎯 Use Cases

1. Agent Training: Train code generation and debugging agents on realistic software engineering tasks
2. Benchmark Evaluation: Evaluate model performance on multi-language software issues

🔗 Related Datasets

• MEnvData-SWE-Trajectory: Extended version with 3,872 agent execution trajectories

📖 Citation

If MEnvData-SWE helps your research, please cite:

@misc{guo2026menvagent,
      title={MEnvAgent: Scalable Polyglot Environment Construction for Verifiable Software Engineering}, 
      author={Chuanzhend Jingjing Wu and Sijun He and Yang Chen and Zhaoqi Kuang and Shilong Fan and Bingjin Chen and Siqi Bao and Jing Liu and Hua Wu and Qingfu Zhu and Wanxiang Che and Haifeng Wang},
      year={2026},
      url={https://arxiv.org/abs/2601.22859}, 
}

📧 Contact

For questions or issues:

• GitHub: MEnvAgent Repository
• Email: czguo@ir.hit.edu.cn

🙏 Acknowledgements

We thank all open-source maintainers whose projects contributed to this dataset.