LLM4MDG: Leveraging Large Language Model to Construct Microservices Dependency Graph

Microservices architecture has gained popularity in modern software development due to its scalability and flexibility. However, understanding the complexity of interactions and dependencies between services presents significant challenges, which complicates the identification and analysis of errors within microservice applications. To gain insights into the architecture and interdependencies of microservices applications, prior studies have developed dependency graphs to illustrate the relationships among services. However, the methods used to construct these dependency graphs are not suitable for common microservices applications and suffer from insufficient data granularity. To address these shortcomings, we introduce LLM4MDG, an in-novative framework for constructing microservices dependency graphs using an LLM-driven multi-agent system. By leveraging optimized prompt engineering and principles of knowledge graphs, LLM4MDG can effectively identify and interpret service interactions across diverse microservice ecosystems, achieving high accuracy and adaptability across various scenarios. We also present a new open-source dataset comprising 47 microservices applications, annotated by domain experts, to validate our frame-work. Evaluation results demonstrate that LLM4MDG achieves an 88.3% accuracy in identifying data dependencies in the Train Ticket project, a benchmark application with over 80 service instances. This study provides a robust solution for constructing dependency graphs and facilitating better system understanding and management.