AI-Driven Approach to Detect Equivalent Elements Within Domain Models

Domain model stands as a crucial part of software engineering, emerging from collaborative team efforts. Domain modeling involves creating a conceptual representation of a specific problem domain to determine the concepts and relationships. A pivotal step in domain modeling is reviewing the domain model to identify errors and abnormalities. Typically, software engineers engage in a manual review of the domain model diagrams for refinement purposes. However, the process of detecting errors and abnormalities can be time-consuming and error prone. Furthermore, it relies heavily on the expertise of software engineers. A primary concern in domain modeling is the repetition of concepts, which often occurs due to the involvement of multiple engineers. Recently, AI techniques have exhibited remarkable ability in modeling domain. This paper proposes an approach, called ARDEMIS, for equivalence checking that aims to detect semantically similar concepts within domain models. ARDEMIS utilizes the combination of pre-trained model and dictionary to identify equivalent elements. We assess our approach using a real-world case study in the transportation domain. Our findings reveal the ability of ARDEMIS to identify potential equivalent elements.