[2603.23073] Can an LLM Detect Instances of Microservice Infrastructure Patterns?

Computer Science > Software Engineering arXiv:2603.23073 (cs) [Submitted on 24 Mar 2026] Title:Can an LLM Detect Instances of Microservice Infrastructure Patterns? Authors:Carlos Eduardo Duarte, Neil B. Harrison, Filipe Figueiredo Correia, Ademar Aguiar, Pavlína Gonçalves View a PDF of the paper titled Can an LLM Detect Instances of Microservice Infrastructure Patterns?, by Carlos Eduardo Duarte and 4 other authors View PDF HTML (experimental) Abstract:Architectural patterns are frequently found in various software artifacts. The wide variety of patterns and their implementations makes detection challenging with current tools, especially since they often only support detecting patterns in artifacts written in a single language. Large Language Models (LLMs), trained on a diverse range of software artifacts and knowledge, might overcome the limitations of existing approaches. However, their true effectiveness and the factors influencing their performance have not yet been thoroughly examined. To better understand this, we developed MicroPAD. This tool utilizes GPT 5 nano to identify architectural patterns in software artifacts written in any language, based on natural-language pattern descriptions. We used MicroPAD to evaluate an LLM's ability to detect instances of architectural patterns, particularly infrastructure-related microservice patterns. To accomplish this, we selected a set of GitHub repositories and contacted their top contributors to create a new, human-annotate...