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F. Rinker, L. Waltersdorfer, M. Schüller, S. Biffl, D. Winkler:
"A Multi-Model Reviewing Approach for Production Systems Engineering Models";
in: "Model-Driven Engineering and Software Development. MODELSWARD 2020. Communications in Computer and Information Science, vol 1361", Springer, Schweiz, 2021, ISBN: 978-3-030-67444-1, 121 - 146.

Background. In Production Systems Engineering (PSE) models, which describe plants, represent different views on several engineering disciplines (such as mechanical, electrical and software engineering)
and may contain up to 10,000s of instance elements, such as concepts,
attributes and relationships. Validating these models requires an integrated multi-model view and the domain expertise of human experts
related to individual views. Unfortunately, the heterogeneity of disciplines, tools, and data formats makes it hard to provide a technologyindependent multi-model view. Aim. In this paper, we aim at improving
Multi-Model Reviewing (MMR) capabilities of domain experts based on
selected model visualisation methods and mechanisms. Method. We (a)
derive requirements for graph-based visualisation to facilitate reviewing multi-disciplinary models; (b) introduce the MMR approach to visualise engineering models for review as hierarchical and linked structures;
(c) design an MMR software prototype; and (d) evaluate the prototype
based on tasks derived from real-world PSE use cases. For evaluation
purposes we compare capabilities of the MMR prototype and a textbased model editor. Results. The MMR prototype enabled performing
the evaluation tasks in most cases considerable faster than the standard
text-based model editor. Conclusion. The promising results of the MMR
approach in the evaluation context warrant empirical studies with a wider
range of domain experts and use cases on the usability and usefulness of
the MMR approach in practice.

Multi-disciplinary engineering visualisation · Production systems engineering · Model-driven engineering · Domain-specific modeling · Model review · Model quality assurance

http://dx.doi.org/10.1007/978-3-030-67445-8_6