SWE-at-Scale: Scalable Issue Synthesis from Historical Bug Fixes

As coding agents become increasingly capable, robust evaluation requires a broader and more diverse supply of verifiable software engineering (SWE) tasks. However, real-world tasks are constrained by the natural pace at which issues arise and the substantial cost of manual curation. Furthermore, existing synthetic approaches rely primarily on the parametric knowledge of large models combined with heuristic prompting, often failing to capture the authentic and complex mechanisms underlying software failures. To scale SWE task production, we introduce SWE-at-Scale, a framework that automatically synthesizes realistic issues by reverse-engineering real-world software fixes. Our key insight is that historical fixes encode mechanistic knowledge of bug formation: reversing a fix transforms repair supervision into bug-induction supervision grounded in actual development scenarios. SWE-at-Scale transfers these learned bug patterns to new code contexts and repeatedly samples diverse bug types to construct executable SWE tasks. Extensive experiments show that SWE-at-Scale generates more diverse and realistic SWE tasks than existing synthesis baselines while posing greater challenges to state-of-the-art coding agents. Notably, the model can generate distinct bug types from the same code context, with bug diversity continuing to increase even after 200 generations per file. Overall, SWE-at-Scale turns finite real-world fixes into reusable supervision for scalable synthesis of realistic, diverse, and challenging SWE tasks.