AI RESEARCH
Large Language Models for Analyzing Enterprise Architecture Debt in Unstructured Documentation
arXiv CS.LG
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ArXi:2604.00046v1 Announce Type: cross Enterprise Architecture Debt (EA Debt) arises from suboptimal design decisions and misaligned components that can degrade an organization's IT landscape over time. Early indicators, Enterprise Architecture Smells (EA Smells), are currently mainly detected manually or only from structured artifacts, leaving much unstructured documentation under-analyzed. This study proposes an approach using a large language model (LLM) to identify and quantify EA Debt in unstructured architectural documentation.