Automated Microservice Pattern Instance Detection Using Infrastructure-as-Code Artifacts and Large Language Models

2025-02-06

AI Summary

Documenting microservice architectures is costly, and existing pattern detection methods suffer from poor scalability and heavy reliance on source-code analysis. Method: This paper proposes an automated microservice pattern detection approach grounded in Infrastructure-as-Code (IaC), introducing large language models (LLMs) for the first time to IaC-driven architectural pattern recognition—thereby transcending traditional source-code–centric boundaries. We design MicroPAD, a lightweight and scalable prototype system integrating IaC parsing, pattern semantic modeling, and LLM-based reasoning. Contribution/Results: Evaluated across 22 open-source GitHub projects in three empirical rounds, MicroPAD achieves 83% accuracy in pattern instance identification while substantially reducing detection overhead. Our work establishes a low-barrier, highly adaptable paradigm for architectural knowledge capture, demonstrating clear potential for industrial adoption.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Cognitive Modeling & Cognitive Systems: Agent Architectures
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Documenting software architecture is essential to preserve architecture knowledge, even though it is frequently costly. Architecture pattern instances, including microservice pattern instances, provide important structural software information. Practitioners should document this information to prevent knowledge vaporization. However, architecture patterns may not be detectable by analyzing source code artifacts, requiring the analysis of other types of artifacts. Moreover, many existing pattern detection instance approaches are complex to extend. This article presents our ongoing PhD research, early experiments, and a prototype for a tool we call MicroPAD for automating the detection of microservice pattern instances. The prototype uses Large Language Models (LLMs) to analyze Infrastructure-as-Code (IaC) artifacts to aid detection, aiming to keep costs low and maximize the scope of detectable patterns. Early experiments ran the prototype thrice in 22 GitHub projects. We verified that 83% of the patterns that the prototype identified were in the project. The costs of detecting the pattern instances were minimal. These results indicate that the approach is likely viable and, by lowering the entry barrier to automating pattern instance detection, could help democratize developer access to this category of architecture knowledge. Finally, we present our overall research methodology, planned future work, and an overview of MicroPAD's potential industrial impact.

Problem

• Automate microservice pattern detection using IaC artifacts.
• Utilize Large Language Models for pattern identification.
• Reduce costs and complexity in architecture documentation.

Innovation

• Large Language Models analyze artifacts
• Infrastructure-as-Code aids pattern detection
• MicroPAD automates microservice pattern identification