From Textual Requirements to Microservice Architectures: A Comprehensive Evaluation of LLM-Based Design Synthesis

Abstract

[Context]. Microservice architectures have become a dominant paradigm for modernizing monolithic systems. However, identifying appropriate services remains a challenging and largely manual task. Existing decomposition approaches are predominantly code-centric, limiting their applicability in early design stages where only textual requirements are available. [Problem]. Despite recent advances in Large Language Models (LLMs), there is still limited empirical evidence regarding their ability to synthesize complete microservice architectures directly from natural-language requirements, including both service definitions and inter-service interactions. [Goal]. This study investigates whether an LLM can bridge the gap between requirements engineering and architectural design by generating microservice architectures solely from textual requirements, and evaluates the structural agreement and perceived quality of the generated solutions. [Method]. We conduct a mixed-method study using the OpenAI o3 model under zero-shot (ZS) and few-shot (FS) prompting strategies across two systems (Bookstore and PetClinic), with one execution per system and prompting condition. Generated architectures are evaluated through (i) quantitative comparison with implemented reference architectures using precision, recall, and F1-score for service identification and communication recovery, and (ii) a blinded expert assessment of correctness, completeness, modularity, and plausibility, supplemented by a structured descriptive synthesis of open-ended feedback. [Results]. The findings indicate that OpenAI o3 can identify services from requirements, achieving higher agreement with the references under FS prompting (F1 ≈ 0.79 for ZS and ≈ 0.97 for FS). Communication recovery is more challenging, with ZS producing overly dense architectures characterized by high recall but low precision (F1 ≈ 0.61). FS prompting improves structural agreement and perceived architectural quality, achieving F1 ≈ 0.82 for communication recovery while reducing unsupported dependencies. Expert evaluation corroborates these findings, indicating that FS-generated architectures are consistently perceived as more modular, coherent, and plausible than ZS outputs. [Conclusion]. Within the evaluated scope, OpenAI o3 shows potential to support requirements-driven architectural synthesis, particularly when guided by minimal exemplar-based prompting. The results should be interpreted as model- and context-specific evidence from two relatively small systems and a single execution per condition, rather than as model-independent proof of effectiveness.