This paper explores how large language models (LLMs) can streamline microservice development using Spring Boot by automating boilerplate code, enhancing API documentation, and suggesting design patterns. The methodology integrates GPT-3.5 and Codex models with Spring Boot development workflows through custom IDE plugins and CI/CD pipeline integration. A comprehensive case study involving enterprise application development demonstrates significant productivity gains, with 40% reduction in development time and 25% improvement in code quality metrics. The study includes evaluation of generated code quality, documentation accuracy, and developer productivity across multiple microservice development scenarios. Results show that LLM-assisted development maintains high code quality while substantially reducing repetitive programming tasks, establishing a foundation for AI-augmented software engineering practices in enterprise environments.
Udaya Kumar Reddy Veeramreddygari (Thu,) studied this question.
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