--- license: mit 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: created_at 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 splits: - name: train num_bytes: 101732749 num_examples: 6426 download_size: 27722795 dataset_size: 101732749 --- # 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 6,448 Issue-Pull Request pairs sourced from 2,133 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. [link] As an example use case of this dataset, we’ve used SWE-bench-extra instances to generate a dataset of 84,480 trajectories (`nebius/swe-agent-trajectories` [link]). 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, outperforming its parent model `Qwen2.5-72B-Instruct` by 30% relative improvement. 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. | 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 [link]. # Licensing Information All dataset contents are available under the MIT license.