Software engineering agents have advanced rapidly, but scaling their training remains constrained by the limited availability of high-quality executable tasks. Development histories contain valuable signals for constructing such tasks, yet they often require substantial reconstruction due to incomplete environments, missing tests, and inconsistent artifacts. We present SWE-Stream, a system for scaling executable SWE training data through progressive construction and elastic streaming execution. SWE-Stream progressively validates and refines task candidates, reusing existing artifacts when possible and applying targeted completion only when necessary. An elastic scheduler asynchronously coordinates construction stages under shared resource constraints, adapting execution budgets based on observed workload demands. Using development histories from 10,728 repositories across six programming languages, SWE-Stream constructs 80,874 validated tasks and collects 118,688 successful trajectories. Fine-tuning Qwen3-30B-A3B-Thinking-2507 on these trajectories achieves resolve rates of 69.0%, 49.3%, and 45.42% on SWE-bench Verified, Multilingual, and Pro, respectively. In a controlled comparison with approximately matched training budgets, SWE-Stream achieves a 45.6% resolve rate on SWE-bench Verified, outperforming three existing data sources. Moreover, its streaming construction strategy improves efficiency, delivering $1.45\times$ the accepted-task throughput of batch construction and accepting 13.2% more tasks than fixed-limit streaming.
SWE-Dev: Evaluating and Training Autonomous Feature-Driven Software Development
Shanghai Jiao Tong University（上海交通大学）；Beijing University of Aeronautics and Astronautics（北京航空航天大学）；The Chinese University of Hong Kong（香港中文大学）；University of Michigan（密歇根大学）
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-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）
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）
