TIGER-Lab/new_commit_better_repos

SWE-Next: Scalable Real-World Software Engineering Tasks for Agents

new_commit_better_repos

This repository contains new_commit_better_repos.csv, an intermediate SWE-Next metadata artifact listing repositories with at least one observed NEW_COMMIT_BETTER commit pair during collection. Each row records a GitHub repository and the number of commit pairs in that repository that produced strict test improvements without regressions.

The file contains 335 repositories and is used by the SWE-Next pipeline as a lightweight index of promising repositories before final task packaging.

Overview

SWE-Next starts from 3,971 seeded Python repositories and executes 102,582 candidate base/merged commit pairs mined from real merged PRs. During this process, repositories that exhibit at least one NEW_COMMIT_BETTER outcome are tracked in this CSV. The file therefore serves as an upstream repository-level summary rather than the final released task dataset.

Format

The CSV has two columns:

Column	Description
repo	GitHub repository in owner/repo format
NEW_COMMIT_BETTER	Number of commit pairs in that repository classified as NEW_COMMIT_BETTER

Example rows:

repo,NEW_COMMIT_BETTER
pydantic/pydantic,152
yt-dlp/yt-dlp,62
pytest-dev/pyfakefs,56

Files

• new_commit_better_repos.csv: repository-level summary of observed NEW_COMMIT_BETTER counts

Usage

This artifact is mainly useful for:

• inspecting which repositories contribute execution-grounded improvements,
• selecting promising repositories for further pipeline runs,
• reproducing intermediate repository-level filtering stages in SWE-Next.

Load it with pandas:

import pandas as pd

df = pd.read_csv("hf://datasets/TIGER-Lab/new_commit_better_repos/new_commit_better_repos.csv")
print(df.head())

Relationship to the SWE-Next Release

This repo contains a repository-level intermediate artifact used by SWE-Next. Related artifacts are available separately:

• Seed repository list: TIGER-Lab/packages_python_filtered
• Final task dataset: TIGER-Lab/SWE-Next
• SFT trajectories: TIGER-Lab/SWE-Next-SFT-Trajectories
• Project code: github.com/TIGER-AI-Lab/SWE-Next

Citation

@misc{liang2026swenextscalablerealworldsoftware,
      title={SWE-Next: Scalable Real-World Software Engineering Tasks for Agents},
      author={Jiarong Liang and Zhiheng Lyu and Zijie Liu and Xiangchao Chen and Ping Nie and Kai Zou and Wenhu Chen},
      year={2026},
      eprint={2603.20691},
      archivePrefix={arXiv},
      primaryClass={cs.SE},
      url={https://arxiv.org/abs/2603.20691},
}