Can an LLM Detect Instances of Microservice Infrastructure Patterns?
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 ...
| Authors | Carlos Eduardo Duarte, Neil B. Harrison, Filipe Figueiredo Correia et al. |
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| Primary category | cs.SE |
| Published | 24 March 2026 |
| Last updated at source | 24 March 2026 |
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