metadata

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:

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. [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

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.