test_ldrb / README.md
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Apply Harbor validation fixes
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---
dataset_info:
features:
- name: repo
dtype: string
- name: instance_id
dtype: string
- name: base_commit
dtype: string
- name: patch
dtype: string
- name: test_patch
dtype: string
- name: problem_statement
dtype: string
- name: hints_text
dtype: string
- name: created_at
dtype: string
- name: version
dtype: string
- name: meta
struct:
- name: commit_name
dtype: string
- name: failed_lite_validators
list: string
- name: has_test_patch
dtype: bool
- name: is_lite
dtype: bool
- name: num_modified_files
dtype: int64
- name: install_config
struct:
- name: docker_specs
dtype: 'null'
- name: env_vars
dtype: 'null'
- name: env_yml_path
list: string
- name: install
dtype: string
- name: log_parser
dtype: string
- name: no_use_env
dtype: bool
- name: old_test_cmd
dtype: string
- name: packages
dtype: string
- name: pip_packages
list: string
- name: pre_install
list: string
- name: python
dtype: string
- name: reqs_path
list: string
- name: test_cmd
dtype: string
- name: try_log_parser
list: string
- name: FAIL_TO_PASS
list: string
- name: PASS_TO_PASS
list: string
- name: environment_setup_commit
dtype: string
- name: docker_image
dtype: string
- name: image_name
dtype: string
- name: interface
dtype: string
- name: harbor_cpus
dtype: int64
- name: harbor_memory
dtype: string
- name: harbor_storage
dtype: string
- name: harbor_verifier_timeout_sec
dtype: int64
splits:
- name: test
num_bytes: 18793979
num_examples: 860
- name: '2025_01'
num_bytes: 1969930
num_examples: 109
- name: '2025_02'
num_bytes: 1245676
num_examples: 76
- name: '2025_03'
num_bytes: 880484
num_examples: 62
- name: '2025_04'
num_bytes: 493281
num_examples: 40
- name: '2025_05'
num_bytes: 695991
num_examples: 40
- name: '2025_06'
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num_examples: 40
- name: '2025_07'
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num_examples: 30
- name: '2025_08'
num_bytes: 660806
num_examples: 52
- name: '2025_09'
num_bytes: 946634
num_examples: 50
- name: '2025_10'
num_bytes: 882737
num_examples: 51
- name: '2025_11'
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num_examples: 47
- name: '2025_12'
num_bytes: 1446103
num_examples: 48
- name: '2026_01'
num_bytes: 4063444
num_examples: 48
- name: '2026_02'
num_bytes: 1242742
num_examples: 57
- name: '2026_03'
num_bytes: 2443314
num_examples: 110
download_size: 10296402
dataset_size: 37587986
configs:
- config_name: default
data_files:
- split: test
path: data/test-*
- split: '2025_01'
path: data/2025_01-*
- split: '2025_02'
path: data/2025_02-*
- split: '2025_03'
path: data/2025_03-*
- split: '2025_04'
path: data/2025_04-*
- split: '2025_05'
path: data/2025_05-*
- split: '2025_06'
path: data/2025_06-*
- split: '2025_07'
path: data/2025_07-*
- split: '2025_08'
path: data/2025_08-*
- split: '2025_09'
path: data/2025_09-*
- split: '2025_10'
path: data/2025_10-*
- split: '2025_11'
path: data/2025_11-*
- split: '2025_12'
path: data/2025_12-*
- split: '2026_01'
path: data/2026_01-*
- split: '2026_02'
path: data/2026_02-*
- split: '2026_03'
path: data/2026_03-*
license: cc-by-4.0
tags:
- code
size_categories:
- n<1K
---
# Dataset Summary
SWE-rebench-leaderboard is a continuously updated, curated subset of the full [SWE-rebench](https://huggingface.co/datasets/nebius/SWE-rebench) corpus, tailored for benchmarking software engineering agents on real-world tasks.
These tasks are used in the [SWE-rebench leaderboard](https://swe-rebench.com/leaderboard). For more details on the benchmark methodology and data collection process, please refer to our paper [SWE-rebench: An Automated Pipeline for Task Collection and Decontaminated Evaluation of Software Engineering Agents](https://arxiv.org/abs/2505.20411).
All Docker images required to run the tasks are pre-built and publicly available on [Docker Hub](https://hub.docker.com/repositories/swerebench). You do not need to build them yourself. The specific image for each task is listed in the `docker_image` column.
To get the exact subset of tasks used for a specific month's SWE-rebench-leaderboard, you can filter the dataset by the `created_at` field.
# News
[2025/09/19] Added a split for each month.
[2025/09/01] Added 52 August tasks, each with a corresponding Docker image.
[2025/08/04] Added 34 July tasks, each with a corresponding Docker image.
# How to Use
```python
from datasets import load_dataset
ds = load_dataset('nebius/SWE-rebench-leaderboard')
ds_june_2025 = ds['test'].filter(lambda x: x['created_at'].startswith('2025-06'))
```
# 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.
| `docker_image` | str | Docker image name for the instance. |
To execute tasks from SWE-rebench (i.e., set up their environments, apply patches, and run tests), we provide a [fork](https://github.com/SWE-rebench/SWE-bench-fork) of the original SWE-bench execution framework, adapted for our dataset's structure and features.
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](https://github.com/SWE-rebench/SWE-bench-fork/commit/980d0cca8aa4e73f1d9f894e906370bef8c4de8a)
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
```bibtex
@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}
}