Pierre Maier

Multi-level modeling (MLM) is typically characterized as minimizing accidental complexity. We observe that MLM levels also aid in managing essential model complexity by separating and organizing model content based on the domain classification level it represents. Building on this insight, we generalize the notion of a level—and previously recognized pluralities of elements, e.g., those contained in packages—to that of a model segment. In addition to solution-based segments, we recognize domain-induced segments, such as domain diversity, allowing semantic information to be represented in models that was previously lost to mapping different modeler intents to the same modeling constructs. These new, semantically relevant, model segments support better model navigability and aid model understanding by facilitating the isolation of particular model aspects. Crucially, all model segments are subject to being represented by proxies, thus enabling a significant reduction of first-contact model complexity. We posit that introducing segments for managing model complexity has the potential to lower the threshold of MLM adoption due to its ability to counteract the often-held perception of MLM models being complex.

Motivated by the importance of object-oriented modeling in education, we introduce UML++, an object-modeling language that supports instantiation and execution of object models at run time. UML++ is complemented by a modeling tool, UML-MX©, that aims at improving the learning of object-oriented modeling by making modeling more tangible and engaging for students. We outline eight requirements for UML-MX© and present its core features with a focus on the components tailored to levitate learning experiences. A preliminary evaluation indicates that UML-MX© is able to meet most requirements and may significantly aid students in the appreciation and comprehension of object-oriented modeling. Future work will focus on further evaluating the tool's effectiveness in a course setting, refining current features for broader educational use, and expanding its support to other areas of conceptual modeling.

This paper is a response to the MULTI 2022 Collaborative Comparison Challenge [23]. We compare FMML^x- and DLM-based solutions. We first present each approach and solution separately, and then discuss trade-offs of both the solutions and the approaches.