DDD-Enforcer: An AI-Powered Multi-Agent System for Real-Time Domain-Driven Design Enforcement


Dincoguz A. B., Kendir A., Karakaya K. M.

5th International Conference on Informatics and Software Engineering, IISEC 2026, Ankara, Turkey, 5 - 06 February 2026, pp.746-751, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/iisec69317.2026.11418529
  • City: Ankara
  • Country: Turkey
  • Page Numbers: pp.746-751
  • Keywords: Domain-Driven Design, Large Language Models, Multi-Agent Systems, Retrieval-Augmented Generation, Software Architecture, Static Analysis, Technical Debt, VS Code Extension
  • TED University Affiliated: Yes

Abstract

Domain Model Degradation increases architectural technical debt by violating ubiquitous language and bounded contexts. This paper presents DDD-Enforcer, a VS Code extension that enforces DDD principles through real-time analysis combining Abstract Syntax Tree (AST) inspection with Large Language Models (LLMs). The system employs a four-stage multi-agent architecture to extract domain models from SRS-related documents and integrates a Retrieval-Augmented Generation (RAG) pipeline for requirement traceability. Experiments demonstrate 100% detection accuracy across 15 violation cases with an average latency of 4.49 seconds. The RAG component achieves 76.8% Top-1 accuracy, remaining robust under noisy conditions. Additionally, preliminary results for the MVC Test Orchestrator suggest potential for automated testing workflows. These findings confirm that hybrid LLM-static analysis effectively maintains DDD compliance.