README.md

metadata

license: cc-by-4.0
task_categories:
  - other
library_name: datasets
dataset_info:
  features:
    - name: instance_id
      dtype: string
    - name: base_commit
      dtype: string
    - name: created_at
      dtype: string
    - name: environment_setup_commit
      dtype: string
    - name: hints_text
      dtype: string
    - name: patch
      dtype: string
    - name: problem_statement
      dtype: string
    - name: repo
      dtype: string
    - name: test_patch
      dtype: string
    - name: meta
      struct:
        - name: commit_name
          dtype: string
        - name: failed_lite_validators
          sequence: string
        - name: has_test_patch
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        - name: is_lite
          dtype: bool
        - name: llm_score
          struct:
            - name: difficulty_score
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            - name: issue_text_score
              dtype: int64
            - name: test_score
              dtype: int64
        - name: num_modified_files
          dtype: int64
    - name: version
      dtype: string
    - name: install_config
      struct:
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            - name: JUPYTER_PLATFORM_DIRS
              dtype: string
        - name: env_yml_path
          sequence: string
        - name: install
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        - name: log_parser
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        - name: no_use_env
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        - name: packages
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        - name: pip_packages
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        - name: pre_install
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        - name: python
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        - name: reqs_path
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        - name: test_cmd
          dtype: string
    - name: requirements
      dtype: string
    - name: environment
      dtype: string
    - name: FAIL_TO_PASS
      sequence: string
    - name: FAIL_TO_FAIL
      sequence: string
    - name: PASS_TO_PASS
      sequence: string
    - name: PASS_TO_FAIL
      sequence: string
    - name: license_name
      dtype: string
    - name: __index_level_0__
      dtype: int64
  splits:
    - name: test
      num_bytes: 737537372
      num_examples: 21336
  download_size: 239735457
  dataset_size: 737537372
configs:
  - config_name: default
    data_files:
      - split: test
        path: data/test-*

Dataset Summary

SWE-rebench is a large-scale dataset designed to support training and evaluation of LLM-based software engineering (SWE) agents, building upon and expanding our earlier release, SWE-bench-extra. It is constructed using a fully automated pipeline that continuously extracts real-world interactive SWE tasks from GitHub repositories at scale, as detailed in our paper SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents. The dataset currently comprises over 21,000 issue–pull request pairs from 3,400+ Python repositories, each validated for correctness through automated environment setup and test execution. A curated subset of these tasks also forms the basis of our continuously updated SWE-rebench leaderboard. SWE-rebench builds upon and extends the methodology of SWE-bench by incorporating several key enhancements detailed in our paper, including:

• A fully automated pipeline for continuous task collection.
• LLM-driven extraction and validation of environment installation instructions.
• An automated LLM-based task quality assessment pipeline that annotates tasks with labels such as clarity, complexity, or test patch validity.

We’ve released 7,500 pre-built Docker images used in our RL pipeline. They’re publicly available on Docker Hub. You do not need to build them yourself.

News

[2025/08/05] Uploaded the corresponding Docker images for 7,500 tasks to Docker Hub.

How to Use

from datasets import load_dataset
ds = load_dataset('nebius/SWE-rebench')

Dataset Structure

The SWE-rebench dataset schema extends the original SWE-bench schema with additional fields to support richer analysis. The complete schema is detailed in the table below. For more information about this data and methodology behind collecting it, please refer to our paper.

Field name	Type	Description
instance_id	str	A formatted instance identifier, usually as repo_owner__repo_name-PR-number.
patch	str	The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue.
repo	str	The repository owner/name identifier from GitHub.
base_commit	str	The commit hash of the repository representing the HEAD of the repository before the solution PR is applied.
hints_text	str	Comments made on the issue prior to the creation of the solution PR’s first commit creation date.
created_at	str	The creation date of the pull request.
test_patch	str	A test-file patch that was contributed by the solution PR.
problem_statement	str	The issue title and body.
version	str	Installation version to use for running evaluation.
environment_setup_commit	str	Commit hash to use for environment setup and installation.
FAIL_TO_PASS	str	A JSON list of strings that represent the set of tests resolved by the PR and tied to the issue resolution.
PASS_TO_PASS	str	A JSON list of strings that represent tests that should pass before and after the PR application.
meta	str	A JSON dictionary indicating whether the instance is lite, along with a list of failed lite validators if it is not.
license_name	str	The type of license of the repository.
install_config	str	Installation configuration for setting up the repository.
requirements	str	Freezed requirements for the repository.
environment	str	Environment configuration for the repository.

To execute tasks from SWE-rebench (i.e., set up their environments, apply patches, and run tests), we provide a fork of the original SWE-bench execution framework, adapted for our dataset's structure and features. Our fork is based on the SWE-bench framework, specifically from its Release 4.0.3. The primary modification introduces functionality to source environment installation constants directly from the install_config field present in each task instance within SWE-rebench. This allows for more flexible and task-specific environment setups.

You can find the details of this modification in the following commit:

To build the necessary Docker images and run agents on SWE-rebench tasks, you have two main options:

1. Use our SWE-bench fork directly: Clone the fork and utilize its scripts for building images and executing tasks. The framework will automatically use the install_config from each task.
2. Integrate similar functionality into your existing codebase: If you have your own execution framework based on SWE-bench or a different system, you can adapt it by implementing a similar mechanism to parse and utilize the install_config field from the SWE-rebench task instances. The aforementioned commit can serve as a reference for this integration.

License

The dataset is licensed under the Creative Commons Attribution 4.0 license. However, please respect the license of each specific repository on which a particular instance is based. To facilitate this, the license of each repository at the time of the commit is provided for every instance.

Citation

@misc{badertdinov2025swerebenchautomatedpipelinetask,
      title={SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents}, 
      author={Ibragim Badertdinov and Alexander Golubev and Maksim Nekrashevich and Anton Shevtsov and Simon Karasik and Andrei Andriushchenko and Maria Trofimova and Daria Litvintseva and Boris Yangel},
      year={2025},
      eprint={2505.20411},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2505.20411}
}