LLM-Generated Microservice Implementations from RESTful API Definitions
Not on the map. Why?
Abstract (arXiv)
The growing need for scalable, maintainable, and fast-deploying systems has made microservice architecture widely popular in software development. This paper presents a system that uses Large Language Models (LLMs) to automate the API-first development of RESTful microservices. This system assists in creating OpenAPI specification, generating server code from it, and refining the code through a feedback loop that analyzes execution logs and error messages. By focusing on the API-first methodology, this system ensures that microservices are designed with well-defined interfaces, promoting consistency and reliability across the development life-cycle. The integration of log analysis enables the LLM to detect and address issues efficiently, reducing the number of iterations required to produce functional and robust services. This process automates the generation of microservices and also simplifies the debugging and refinement phases, allowing developers to focus on higher-level design and integration tasks. This system has the potential to benefit software developers, architects, and organizations to speed up software development cycles and reducing manual effort. To assess the potential of the system, we conducted surveys with six industry practitioners. After surveying practitioners, the system demonstrated notable advantages in enhancing development speed, automating repetitive tasks, and simplifying the prototyping process. While experienced developers appreciated its efficiency for specific tasks, some expressed concerns about its limitations in handling advanced customizations and larger scale projects. The code is publicly available at https://github.com/sirbh/code-gen
Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). First posted on arXiv 13 Feb 2025.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2025: 3; 2026: 2.
5 papers.
- 2025Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AIcited by 42
- 2025Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfacescited by 2
- 2025From Specification to Service: Accelerating API-First Development Using Multi-Agent Systemscited by 0
- 2026LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Studycited by 0
- 2026Quantifying Divergence in Inter-LLM Communication Through API Retrieval and Rankingcited by 0
- 2026LLM-Powered Workflow Optimization for Multidisciplinary Software Development: An Automotive Industry Case Study22 Mar 2026
- 2026Quantifying Divergence in Inter-LLM Communication Through API Retrieval and Ranking9 Mar 2026
- 2025From Specification to Service: Accelerating API-First Development Using Multi-Agent Systems22 Oct 2025
- 2025Vibe Coding vs. Agentic Coding: Fundamentals and Practical Implications of Agentic AI26 May 2025
- 2025Capability-Driven Skill Generation with LLMs: A RAG-Based Approach for Reusing Existing Libraries and Interfaces6 May 2025