Thomas Kühne

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.

Multi-level modeling languages differ in their approaches for controlling the properties of model elements over multiple modeling levels. Over the years the original approach for deeply characterizing model elements, the potency-based deep instantiation mechanism, has received a number of criticisms related to its flexibility, level stability, ontological soundness, type safety, and ability to reduce accidental complexity. However, some of these criticisms are founded on misconceptions and thus cannot usefully inform multi-level modeling language designs. In this paper we identify and clarify these misconceptions in order to help guide future considerations of language feature trade-offs and design choices.

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.

© Springer International Publishing Switzerland 2016. The introduction of ontological classification to support domain-metamodeling has been pivotal in the emergence of multi-level modeling as a dynamic research area. However, existing expositions of ontological classification have only used a limited context to distinguish it from the historically more commonly used linguistic classification. In important areas such as domain-specific languages and classic language engineering the distinction can appear to become blurred and the role of ontological classification is obscured, if not fundamentally challenged. In this paper we therefore examine critical points of confusion regarding the distinction and provide an expanded explanation of the differences. We maintain that optimally utilizing ontological classification, even for tasks that traditionally have only been viewed as language engineering, is critical for mastering the challenges in complex systems modeling including the validation of multi-language models.