metadata

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:

Issue and Pull Request Collection: Issues are gathered and linked with pull requests that successfully resolve them.
Filtering: Instances are filtered based on attributes such as issue descriptions, relevant code paths, and test patches.
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.

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. 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”.

How to Use

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.

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.