Simulation and Analysis of Metamodel Designs

Context

The construction of new metamodels is a complex and knowledge-intensive engineering activity. As of now, metamodel design is merely a creative and manual task with a lack of proper tooling support. Metamodel design decisions however determine utility, capabilities, and expressiveness of the conceptual modeling language – and eventually the created models. Designing high-quality metamodels, one should have the support of expressive engineering tools such as theoretically well-founded metamodeling methodologies, patterns and anti-patterns, and automated supporting environments.

 

Task

This project aims at realizing a model-based metamodeling environment that facilitates the simulation of a constructed metamodel. In this respect, metamodel design decisions can be simulated and their consequences interactively investigated during the metamodel design. Example functionality might include the validation of the metamodel, the quantitative and qualitative analysis of the metamodel, and the generation of sample model instances.

Two target audiences shall benefit from this project: i) the scientific metamodeling community shall be supported in designing and validating new metamodels, and ii) the metamodeling and conceptual modeling educators shall use the tool for education purposes.

 

Further Reading (Excerpt)

  • Sales, T. P., & Guizzardi, G. (2015). Ontological anti-patterns: Empirically uncovered error-prone structures in ontology-driven conceptual models. Data & Knowledge Engineering, 99, 72-104.
  • Bork, D., Karagiannis, D., & Pittl, B. (2020). A survey of modeling language specification techniques. Information Systems, 87, 101425.
  • Karagiannis, D., Burzynski, P., Utz, W., & Buchmann, R. A. (2019). A Metamodeling Approach to Support the Engineering of Modeling Method Requirements. In 2019 IEEE 27th International Requirements Engineering Conference (RE) (pp. 199-210). IEEE.
  • Bork, D. (2018). Metamodel-based analysis of domain-specific conceptual modeling methods. In IFIP Working Conference on The Practice of Enterprise Modeling (pp. 172-187). Springer, Cham.
  • https://github.com/nemo-ufes/ontouml-lightweight-editor