Progress in software-engineering agents is increasingly constrained by the scarcity of executable, scalable, and realistic data for training and evaluation. This scarcity stems from three fundamental challenges in existing pipelines: environments are brittle and difficult to reproduce across languages; synthesizing realistic, system-level bugs at scale is computationally expensive; and existing data predominantly consists of short-horizon repairs, failing to capture long-horizon competencies like architectural consistency. We introduce \textbf{SWE-Hub}, an end-to-end system that operationalizes the data factory abstraction by unifying environment automation, scalable synthesis, and diverse task generation into a coherent production stack. At its foundation, the \textbf{Env Agent} establishes a shared execution substrate by automatically converting raw repository snapshots into reproducible, multi-language container environments with standardized interfaces. Built upon this substrate, \textbf{SWE-Scale} engine addresses the need for high-throughput generation, combining cross-language code analysis with cluster-scale validation to synthesize massive volumes of localized bug-fix instances. \textbf{Bug Agent} generates high-fidelity repair tasks by synthesizing system-level regressions involving cross-module dependencies, paired with user-like issue reports that describe observable symptoms rather than root causes. Finally, \textbf{SWE-Architect} expands the task scope from repair to creation by translating natural-language requirements into repository-scale build-a-repo tasks. By integrating these components, SWE-Hub establishes a unified production pipeline capable of continuously delivering executable tasks across the entire software engineering lifecycle.
SWE-Marathon: Can Agents Autonomously Complete Ultra-Long-Horizon Software Work?
Abundant；Zenity；Harvard University（哈佛大学）；University of Waterloo（滑铁卢大学）；Gujarat Technological University（古吉拉特技术大学）；Warping；Stanford University（斯坦福大学）；UNC-Chapel Hill（北卡罗来纳大学教堂山分校）；Independent（独立）；Refresh；Soleda AI；Near AI；Georgia Tech（佐治亚理工学院）；Comenius University in Bratislava（布拉迪斯拉发Comenius大学）；UC San Diego（圣地亚哥大学）；BenchFlow；UC Santa Barbara（圣巴巴拉大学）
SWE-Game: Can Coding Agents Build the Games We Want?
Shanghai Jiao Tong University（上海交通大学）；Zhongguancun Academy（中关村学院）；Shanghai Innovation Institute（上海创新研究院）；Shenzhen University（深圳大学）；Beijing University of Posts and Telecommunications（北京邮电大学）；Elbetech Technology（埃尔贝特科技）
SWE-QA-Pro: A Representative Benchmark and Scalable Training Recipe for Repository-Level Code Understanding
University of Waterloo（滑铁卢大学）；University of Toronto（多伦多大学）；The Hong Kong University of Science and Technology（香港科学与技术大学）；McGill University & MILA（麦吉尔大学及MILA）；Verdent AI, Inc.（Verdent AI公司）
Flower Hub: A Reproducible Benchmarking Platform for Federated Learning in Simulation and Deployment
Flower Labs；University of Cambridge（剑桥大学）；University of Auckland（奥克兰大学）；University of Melbourne（墨尔本大学）；Vector Institute；Fraunhofer IMS（弗劳恩霍夫应用固体物理与材料力学研究所）；NetCompany；Gachon University（嘉泉大学）；Owkin；Sony AI（索尼人工智能）
A Unified Issue Resolution Benchmark for Requirement Clarification, Planning, and Code Generation for Coding Agents
Singapore Management University（新加坡管理大学）；Monash University Malaysia（马来西亚莫纳什大学）；Daegu Gyeongbuk Institute of Science and Technology (DGIST)（大邱庆北科学技术院）；The Chinese University of Hong Kong（香港中文大学）；North Carolina State University（北卡罗来纳州立大学）；City University of Hong Kong（香港城市大学）；The Hong Kong University of Science and Technology（香港科技大学）；The University of Hong Kong（香港大学）
