A LLM-Augmented Code Generation and Optimization Framework for API-First Integration in Java and Python Microservice Pipelines

Abstract

The rapid evolution of microservice-oriented architectures and API-first development paradigms has introduced unprecedented complexity in enterprise integration landscapes, particularly in heterogeneous technology environments dominated by Java and Python service pipelines. While large language models (LLMs) have demonstrated promising capabilities in automated code synthesis, their systematic integration into production-grade software engineering workflows remains underexplored. This paper proposes a comprehensive LLM-augmented framework for intelligent code generation, validation, optimization, and continuous refactoring tailored for API-first microservice pipelines. The framework embeds generative models within structured development lifecycles, incorporating architectural governance, semantic validation, security compliance enforcement, performance optimization, and observability-driven feedback loops. By unifying contract-driven API design, automated code scaffolding, adaptive optimization heuristics, and runtime telemetry-based refinement, the proposed model enables accelerated development cycles, reduced integration defects, and enhanced system resilience. Experimental evaluation across representative Java Spring Boot and Python FastAPI ecosystems demonstrates measurable improvements in development velocity, integration quality, security compliance, and runtime efficiency. The findings establish a scalable blueprint for operationalizing LLM-assisted software engineering within mission-critical enterprise integration platforms.