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--- |
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license: mit |
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configs: |
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- config_name: default |
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data_files: |
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- split: train |
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path: data/train-* |
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dataset_info: |
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features: |
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- name: instance_id |
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dtype: string |
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- name: patch |
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dtype: string |
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- name: repo |
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dtype: string |
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- name: base_commit |
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dtype: string |
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- name: hints_text |
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dtype: string |
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- name: created_at |
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dtype: string |
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- name: test_patch |
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dtype: string |
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- name: problem_statement |
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dtype: string |
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- name: version |
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dtype: string |
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- name: environment_setup_commit |
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dtype: string |
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- name: FAIL_TO_PASS |
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sequence: string |
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- name: PASS_TO_PASS |
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sequence: string |
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- name: meta |
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struct: |
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- name: failed_lite_validators |
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sequence: string |
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- name: has_test_patch |
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dtype: bool |
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- name: is_lite |
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dtype: bool |
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splits: |
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- name: train |
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num_bytes: 101732749 |
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num_examples: 6426 |
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download_size: 27722795 |
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dataset_size: 101732749 |
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--- |
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# Dataset Summary |
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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. |
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# Dataset Description |
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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: |
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1. **Issue and Pull Request Collection**: Issues are gathered and linked with pull requests that successfully resolve them. |
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2. **Filtering**: Instances are filtered based on attributes such as issue descriptions, relevant code paths, and test patches. |
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3. **Execution-based Validation**: The project environments are set up and tests are run to verify that they execute correctly. |
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For a more detailed description of the data collection process, please refer to our blog post. [link] |
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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]. |
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# How to Use |
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```python |
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from datasets import load_dataset |
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ds = load_dataset('nebius/SWE-bench-extra') |
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``` |
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# Dataset Statistics |
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Average, 75th percentile, and maximum values characterizing various attributes of the collected instances. Statistics are micro-averaged without grouping by repository. |
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| Data | Type | Mean | p75 | Max | |
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|---------------|--------------------|----------|----------|-----------| |
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| Issue text | Length (words) | 111.5 | 146 | 1,294 | |
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| Code base | Files (Non-test) | 71.71 | 72.00 | 2,264 | |
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| | Lines (Non-test) | 15,163.38| 13,777 | 1,039,288 | |
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| Gold patch | Files edited | 2.6 | 3 | 7 | |
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| | Lines edited | 56 | 76 | 300 | |
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| Tests | Fail to Pass | 10.94 | 5 | 4,941 | |
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| | Total | 58.5 | 49 | 7,820 | |
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# Dataset Structure |
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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. |
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| Field name | Type | Description | |
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|----------------------------|--------|-------------------------------------------------------------------------------------------------| |
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| `instance_id` | str | A formatted instance identifier, usually as `repo_owner__repo_name-PR-number`. | |
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| `patch` | str | The gold patch, the patch generated by the PR (minus test-related code), that resolved the issue. | |
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| `repo` | str | The repository owner/name identifier from GitHub. | |
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| `base_commit` | str | The commit hash of the repository representing the HEAD of the repository before the solution PR is applied. | |
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| `hints_text` | str | Comments made on the issue prior to the creation of the solution PR’s first commit creation date. | |
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| `created_at` | str | The creation date of the pull request. | |
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| `test_patch` | str | A test-file patch that was contributed by the solution PR. | |
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| `problem_statement` | str | The issue title and body. | |
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| `version` | str | Installation version to use for running evaluation. | |
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| `environment_setup_commit` | str | Commit hash to use for environment setup and installation. | |
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| `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. | |
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| `PASS_TO_PASS` | str | A JSON list of strings that represent tests that should pass before and after the PR application. | |
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| `meta` | str | A JSON dictionary indicating whether the instance is lite, along with a list of failed lite validators if it is not. | |
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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]. |
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# Licensing Information |
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All dataset contents are available under the MIT license. |
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