[2502.04188] Automated Microservice Pattern Instance Detection Using Infrastructure-as-Code Artifacts and Large Language Models

[2502.04188] Automated Microservice Pattern Instance Detection Using Infrastructure-as-Code Artifacts and Large Language Models

arXiv - AI 4 min read

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Abstract page for arXiv paper 2502.04188: Automated Microservice Pattern Instance Detection Using Infrastructure-as-Code Artifacts and Large Language Models

Computer Science > Software Engineering arXiv:2502.04188 (cs) [Submitted on 6 Feb 2025] Title:Automated Microservice Pattern Instance Detection Using Infrastructure-as-Code Artifacts and Large Language Models Authors:Carlos Eduardo Duarte View a PDF of the paper titled Automated Microservice Pattern Instance Detection Using Infrastructure-as-Code Artifacts and Large Language Models, by Carlos Eduardo Duarte View PDF HTML (experimental) 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 identifie...

Originally published on March 25, 2026. Curated by AI News.

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