🤖 AI Summary
This work addresses the lack of formal rules in UML modeling, which often leads to semantic inconsistencies across diagrams and compromises architectural integrity. To resolve this, the paper proposes composite consistency rules that, for the first time, formally encode architects’ design practices into composable and reusable high-level patterns. These patterns enable the automatic derivation of target diagrams through abstraction via natural-language-based rules. The approach has been implemented as an automated JScript extension within Sparx Enterprise Architect, significantly improving modeling consistency and completeness while reducing redundant manual operations. This automation accelerates the design process and enhances the reusability of UML architectures across multiple projects, thereby laying a foundational framework for AI-assisted architectural generation.
📝 Abstract
Unified Modeling Language (UML) is widely used for modeling IT systems but lacks formal rules to ensure consistency across diagrams. This often leads to inconsistencies when shared elements are interpreted differently. To address this, architects use consistency rules that derive elements in target diagrams from more abstract source diagrams. However, these rules are often written in natural language and applied at the element level, making them difficult to reuse or integrate with modeling tools. This paper introduces composite consistency rules-higher-level patterns that combine simple rules into more intuitive, reusable structures. These rules reflect architects design practices and support systematic, error-resistant model development. Implemented as JScript scripts in Sparx Enterprise Architect, they improve automation, reduce redundancy, and accelerate design. Composite rules enhance the consistency and completeness of UML architectures and can be reused across projects. They also support pattern-driven modeling and open possibilities for AI-assisted architecture generation and code integration.