| | --- |
| | configs: |
| | - config_name: default |
| | data_files: |
| | - split: train |
| | path: data/train-* |
| | dataset_info: |
| | features: |
| | - name: instance_id |
| | dtype: string |
| | - name: patch |
| | dtype: string |
| | - name: repo |
| | dtype: string |
| | - name: base_commit |
| | dtype: string |
| | - name: hints_text |
| | dtype: string |
| | - name: test_patch |
| | dtype: string |
| | - name: problem_statement |
| | dtype: string |
| | - name: version |
| | dtype: string |
| | - name: environment_setup_commit |
| | dtype: string |
| | - name: FAIL_TO_PASS |
| | sequence: string |
| | - name: PASS_TO_PASS |
| | sequence: string |
| | - name: meta |
| | struct: |
| | - name: failed_lite_validators |
| | sequence: string |
| | - name: has_test_patch |
| | dtype: bool |
| | - name: is_lite |
| | dtype: bool |
| | - name: created_at |
| | dtype: string |
| | - name: license |
| | dtype: string |
| | - name: __index_level_0__ |
| | dtype: int64 |
| | splits: |
| | - name: train |
| | num_bytes: 87970197 |
| | num_examples: 6376 |
| | download_size: 24675794 |
| | dataset_size: 87970197 |
| | license: cc-by-4.0 |
| | tags: |
| | - code |
| | - synthetic |
| | - tools |
| | - agents |
| | - software |
| | size_categories: |
| | - 1K<n<10K |
| | --- |
| | |
| | *Note: This dataset has an improved and significantly larger successor: [SWE-rebench](https://huggingface.co/datasets/nebius/SWE-rebench).* |
| |
|
| | # Dataset Summary |
| | SWE-bench Extra is a dataset that can be used to train or evaluate agentic systems specializing in resolving GitHub issues. It is based on the methodology used to build SWE-bench benchmark and includes 6,415 Issue-Pull Request pairs sourced from 1,988 Python repositories. |
| |
|
| | # Dataset Description |
| | The SWE-bench Extra dataset supports the development of software engineering agents capable of autonomously solving GitHub issues. The data collection process, based on the SWE-bench methodology, involves the following steps: |
| |
|
| | 1. **Issue and Pull Request Collection**: Issues are gathered and linked with pull requests that successfully resolve them. |
| | 2. **Filtering**: Instances are filtered based on attributes such as issue descriptions, relevant code paths, and test patches. |
| | 3. **Execution-based Validation**: The project environments are set up and tests are run to verify that they execute correctly. |
| |
|
| | For a more detailed description of the data collection process, please refer to our blog post [Scaling data collection for training software engineering agents](https://nebius.com/blog/posts/scaling-data-collection-for-training-swe-agents). |
| |
|
| | As an example use case of this dataset, we’ve used SWE-bench-extra instances to generate a dataset of 80,036 trajectories [`nebius/swe-agent-trajectories`](https://huggingface.co/datasets/nebius/swe-agent-trajectories). We’ve then trained an action generator model, that achieves a score of 19.2% on the subset of 50 random instances from the SWE-bench Verified benchmark, representing a 30% relative improvement over its parent model Qwen2.5-72B-Instruct, which scored 14.8%. Further augmenting the action generator with a guided search based on a critic model, also trained on this data, achieves 40.6% on the full SWE-bench Verified benchmark, which is state-of-the-art among agents using solely open-weight models. You can read more about this agent in our blog post, [“Leveraging Training and Search for Better Software Engineering Agents”](https://nebius.com/blog/posts/training-and-search-for-software-engineering-agents). |
| |
|
| | # How to Use |
| |
|
| | ```python |
| | from datasets import load_dataset |
| | ds = load_dataset('nebius/SWE-bench-extra') |
| | ``` |
| |
|
| | # Dataset Statistics |
| | Average, 75th percentile, and maximum values characterizing various attributes of the collected instances. Statistics are micro-averaged without grouping by repository. |
| |
|
| | | Data | Type | Mean | p75 | Max | |
| | |---------------|--------------------|----------|----------|-----------| |
| | | Issue text | Length (words) | 111.5 | 146 | 1,294 | |
| | | Code base | Files (Non-test) | 71.71 | 72.00 | 2,264 | |
| | | | Lines (Non-test) | 15,163.38| 13,777 | 1,039,288 | |
| | | Gold patch | Files edited | 2.6 | 3 | 7 | |
| | | | Lines edited | 56 | 76 | 300 | |
| | | Tests | Fail to Pass | 10.94 | 5 | 4,941 | |
| | | | Total | 58.5 | 49 | 7,820 | |
| |
|
| | # Dataset Structure |
| | The dataset contains the following fields. It includes all fields from SWE-bench and adds a `meta` column, which indicates whether the instance meets the "lite" criteria and, if not, lists the failed validators. |
| |
|
| | | 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` | str | The type of license of the repository. | |
| |
|
| | To execute instances within SWE-bench, you need to provide a default recipe for dependency installation. The constants required for running these instances are described in this [constants.py](https://huggingface.co/datasets/nebius/SWE-bench-extra/blob/main/constants.py). |
| |
|
| | # 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. |
