Models have evolved from mere pictures supporting human understanding and communication to sophisticated knowledge structures processable by machines and establish value through their processing capabilities. This entails an inevitable need for computer-understandable modeling languages and causes formalization to be a crucial part in the lifecycle of engineering a modeling method. An appropriate formalism must be a means for providing a structural definition to enable a theoretical investigation of conceptual modeling languages and a unique, unambiguous way of specifying the syntax and semantics of an arbitrary modeling language. For this purpose, it must be generic and open to capturing any domain and any functionality. This paper provides a pervasive description of the formalism MetaMorph based on logic and model theory—an approach fulfilling the requirements above for modeling method engineering. The evaluation of the formalism is presented following three streams of work: First, two evaluative case studies illustrate the applicability of MetaMorph formalism concept by concept on the modeling language ProVis from the domain of stochastic education and the well-known Entity-Relationship language. ProVis as well as ER comprise only a few objects and relation types but with high interconnection and expressive power and are therefore considered interesting specimens for formalization. Second, a comprehensive juxtaposition of MetaMorph to three other formalization approaches based on different foundational theories is outlined concept by concept to underpin the formalism design. Third, an empirical evaluation has been performed, assessing the usability and adequacy of the formalism within a classroom assessment. The results allow for conclusions on the completeness, intuitiveness, and complexity as well as on interdependencies with engineers’ skills.
CITATION STYLE
Döller, V., Karagiannis, D., & Utz, W. (2023). MetaMorph: formalization of domain-specific conceptual modeling methods—an evaluative case study, juxtaposition and empirical assessment. Software and Systems Modeling, 22(1), 75–110. https://doi.org/10.1007/s10270-022-01047-4
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