RealSWE: A Compositional Evaluation of Coding Agents under Realistic User Requests
Coding agents are now commonly evaluated on the SWE-bench family of benchmarks, whose tasks are built from curated GitHub issues: long, structured, and information-rich. Real user requests, however, are typically far shorter and less structured. To characterize this gap, we define a six-category information taxonomy and four dimensions of linguistic style, and apply them to real user prompts from SWE-chat and problem statements from SWE-bench Verified and Pro. We find that requests carrying only a problem statement, alone or with limited additional context, account for 88% of real prompts but just 7% of benchmark problems. Furthermore, 87% of real prompts are casually written whereas 94% ...
| Authors | Gyuhyeong Kim, Hyojung Gwon, Jeonghyeon Kim et al. |
|---|---|
| Primary category | cs.AI |
| Published | 28 August 2026 |
| Last updated at source | 5 October 2026 |
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