An Evidence-Based LLM Framework for Expert-Validated Clean Architecture Conformance Assessment in Enterprise Software Systems

Architectural erosion and technical debt make it difficult to preserve intended design principles in long-lived enterprise software systems. Clean Architecture offers principles for separating business rules from frameworks, persistence and interface concerns, but its assessment often remains informal and difficult to scale across heterogeneous repositories. This article presents a hybrid framework for evaluating Clean Architecture conformance using traceable static evidence, a criterion-based rubric and structured large language model (LLM) prompts. The framework was evaluated over an inventoried corpus of 50 open-source enterprise repositories, from which 12 repositories and 36 modules were selected for an expanded validation phase. Two LLM-based configurations produced 1136 auditable candidate findings, reviewed blindly by three expert reviewers and resolved through a 2-of-3 consensus rule, with ordinal-median resolution for eight severity ties. In the final expanded gold standard, all 1136 candidate findings were consensus-supported, no candidate finding reached majority false-positive consensus, 975 rows had sufficient evidence and 161 had partial evidence. The final mean absolute score error was 0.0118 and severity agreement was 0.9525. The results suggest that LLMs can support AI-enhanced software engineering when constrained by evidence, rubrics and expert validation, but should not be treated as autonomous architectural judges.