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---
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: int64
- 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: timestamp[ns, tz=UTC]
- name: license
dtype: string
splits:
- name: train
num_bytes: 88219540
num_examples: 6411
download_size: 24592081
dataset_size: 88219540
license: cc-by-4.0
tags:
- code
- agents
- tools
size_categories:
- 1K<n<10K
---
# 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. |