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• Existing benchmarks for evaluating AI coding agents differ from real production usage in programming language distribution, prompt style, and codebase structure.limitation 90%
• ProdCodeBench is a benchmark built from real sessions with a production AI coding assistant, containing verbatim prompts, committed code changes, and fail-to-pass tests.method 95%
• The methodology for curating ProdCodeBench includes LLM-based task classification, test relevance validation, and multi-run stability checks.method 90%
• Systematic analysis of four foundation models on ProdCodeBench yields solve rates ranging from 53.2% to 72.2%.result 95%
• Models that make greater use of work validation tools, such as executing tests and invoking static analysis, achieve higher solve rates on ProdCodeBench.result 90%
• Iterative verification helps achieve effective agent behavior for AI coding assistants.technical 85%
• Exposing codebase-specific verification mechanisms may significantly improve the performance of externally trained agents operating in unfamiliar environments.technical 85%
• Benchmarks that reflect production workloads are better for evaluating AI coding agents in industrial settings.technical 90%

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