OpenAI o3 Identifies Microservices at F1=0.97 From Requirements Text but Preserves Only 68% of Real Code Dependencies

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Two companion studies published July 30 evaluate LLM-driven microservice design from opposite ends. The first (arXiv 2607.28307) has OpenAI o3 synthesize complete architectures from natural-language requirements for Bookstore and PetClinic, reaching F1=0.79 for service identification under zero-shot prompting versus 0.97 few-shot, validated by blinded expert assessment. The second (arXiv 2607.28331) checks whether such decompositions preserve dependencies actually implemented in source code, and after normalizing for class-to-service mapping coverage both prompting strategies achieve equivalent structural adherence at 68.0% dependency preservation on PetClinic and 83.3% on Bookstore. The pair is a useful caution: semantic plausibility scores high while structural fidelity lags, and apparent prompting-strategy differences can be an artifact of mapping coverage.