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 frequency of issue reporting and the substantial cost of manual curation. Furthermore, existing synthetic approaches rely primarily on the pretrained knowledge of large models coupled with heuristic prompting, often failing to capture the authentic, complex mechanics of software failures. To exponentially 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 i
| Authors | Silin Chen, Zihan Zhang, Xiaodong Gu et al. |
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| Published | 15 October 2026 |
| Last updated at source | — |
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