REAP: Automatic Curation of Coding Agent Benchmarks from Interactive Production Usage
Production deployment of AI coding agents requires fast, reproducible evaluation signals. Existing industrial practices trade off speed and fidelity: online A/B testing takes weeks and risks user experience, shadow deployment yields signals that are not reproducible across runs, and public benchmarks diverge from production workloads in language distribution, prompt style, and codebase structure. This paper presents REAP (Relevance and Execution-Audited Pipeline), an automated curation pipeline that constructs production-derived benchmarks from real developer-agent sessions without manual labeling. Such curation, while in-distribution to production usage, runs into several challenges. Unt...
| Authors | Smriti Jha, Matteo Paltenghi, Chandra Maddila et al. |
|---|---|
| Primary category | cs.SE |
| Published | 2 April 2026 |
| Last updated at source | 28 July 2026 |
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