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

TL;DR

This paper introduces MicroPAD, a prototype tool leveraging Large Language Models to automate detection of microservice pattern instances from Infrastructure-as-Code artifacts, aiming to reduce costs and broaden pattern detection scope.

Contribution

The paper presents a novel approach using LLMs to analyze IaC artifacts for microservice pattern detection, with promising early experimental results demonstrating feasibility.

Findings

83% of detected patterns were verified in projects

Detection costs were minimal

Approach shows potential for broad pattern detection

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.