这篇论文用源码依赖实测OpenAI o3拆微服务的靠谱度，零样本和少样本结果差不多，TPD达到68%和83%。
该论文针对OpenAI o3生成的微服务分解提出基于静态依赖分析的自动验证流程，在PetClinic和Bookstore两个系统上比较零样本与少样本提示策略。使用依赖保持（TPD）和依赖违反（TVD）指标评估，并通过鲁棒性分析控制类到服务映射覆盖差异。标准化后两种策略的结构遵从性等价，TPD值分别为68.0%和83.3%。结果表明，结构评估需显式控制映射覆盖率，否则提示策略间的差异可能是方法偏差。
Structural Validation of LLM-Generated Microservice Decompositions Using Source-Code Dependencies
Decomposing monolithic systems into microservices is a key activity in software modernization. Although Large Language Models (LLMs) can generate semantically plausible decompositions from textual requirements, it remains unclear whether these proposals preserve the structural dependencies implemented in the source code. This paper evaluates the structural adherence of microservice decompositions generated by OpenAI o3 for the PetClinic and Bookstore systems. We propose an automated validation pipeline based on static dependency analysis and compare zero-shot and few-shot prompting using dependency preservation (TPD) and dependency violation (TVD) metrics. A robustness analysis was conducted to control for differences in class-to-service mapping coverage. After normalization, both prompting strategies produced equivalent structural adherence, achieving TPD values of 68.0% (PetClinic) and 83.3% (Bookstore). The findings demonstrate that structural evaluations of LLM-generated decompositions should explicitly control for mapping coverage, as apparent differences between prompting strategies may otherwise reflect methodological bias rather than genuine architectural quality.
- OpenAI07-29 23:57原文