packages_python_filtered

<div align="center"> <h1>SWE-Next: Scalable Real-World Software Engineering Tasks for Agents</h1> </div> <div align="center"> <a href="https://arxiv.org/abs/2603.20691"><img alt="Paper" src="https://img.shields.io/badge/Paper-arXiv-b31b1b?style=for-the-badge&logo=arxiv&logoColor=white"></a> <a href="https://tiger-ai-lab.github.io/SWE-Next/"><img alt="Project Page" src="https://img.shields.io/badge/Project%20Page-Website-4285F4?style=for-the-badge&logo=googlechrome&logoColor=white"></a> <a href="https://github.com/TIGER-AI-Lab/SWE-Next"><img alt="Code" src="https://img.shields.io/badge/Code-GitHub-181717?style=for-the-badge&logo=github&logoColor=white"></a> <a href="https://huggingface.co/datasets/TIGER-Lab/SWE-Next"><img alt="Dataset" src="https://img.shields.io/badge/Base%20Dataset-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a> <a href="https://huggingface.co/datasets/TIGER-Lab/SWE-Next-SFT-Trajectories"><img alt="SFT Trajs" src="https://img.shields.io/badge/SFT%20Trajs-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a> <a href="https://huggingface.co/TIGER-Lab/SWE-Next-7B"><img alt="Model 7B" src="https://img.shields.io/badge/Model%207B-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a> <a href="https://huggingface.co/TIGER-Lab/SWE-Next-14B"><img alt="Model 14B" src="https://img.shields.io/badge/Model%2014B-HuggingFace-FFD21E?style=for-the-badge&logo=huggingface&logoColor=000"></a> </div> # packages_python_filtered This repository contains `packages_python_filtered.csv`, the seed repository list used by SWE-Next. The file contains **3,971** Python package / repository entries that serve as the starting point for large-scale repository mining and execution-grounded task synthesis. Each row links a package-oriented seed entry to a GitHub repository and includes lightweight metadata used by the collection pipeline, such as stars, language, whether the repository was downloaded, and merged-PR counts. ## Overview SWE-Next begins from this seed list of **3,971** repositories, then mines merged pull requests, executes candidate base/merged commit pairs, and filters them into the final execution-grounded SWE dataset. This CSV is therefore the upstream repository inventory that defines the initial search space for the pipeline. ## Format The CSV has the following columns: | Column | Description | |---|---| | `pypi_name` | PyPI package or seed package name associated with the repository | | `repo_name` | GitHub repository in `owner/repo` format | | `local_path` | Original local clone path used during collection | | `stars` | Repository stars at collection time | | `downloaded` | Whether the repository was successfully downloaded | | `primary_language` | Primary repository language | | `pr_count` | Number of pull requests observed for the repository | Example rows: ```csv pypi_name,repo_name,local_path,stars,downloaded,primary_language,pr_count vdtool,yt-dlp/yt-dlp,...,120643,True,Python,4349 django-squad,django/django,...,84411,True,Python,20318 ``` ## Files - `packages_python_filtered.csv`: seed repository list for SWE-Next collection ## Usage This artifact is mainly useful for: - reproducing the initial repository search space of SWE-Next, - analyzing repository-scale coverage before task synthesis, - selecting subsets of repositories for custom collection runs. Load it with pandas: ```python import pandas as pd df = pd.read_csv("hf://datasets/TIGER-Lab/packages_python_filtered/packages_python_filtered.csv") print(df.head()) ``` ## Relationship to the SWE-Next Release This repo contains the seed repository list used by SWE-Next. Related artifacts are available separately: - **Repository summary with NEW_COMMIT_BETTER counts**: `TIGER-Lab/new_commit_better_repos` - **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 ```bibtex @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}, } ```