Conceptual model visual simulation and the inductive learning of missing domain constraints

Conceptual modeling plays a fundamental role in information systems engineering, and in data and systems interoperability. To play their role as instruments for domain modeling, conceptual models must contain the exact set of constraints that represent the worldview of the relevant domain stakeholders. However, as empirical results show, conceptual modelers are subject to cognitive limitations and biases and, hence, in practice, they systematically produce models that fall short in that respect. Moreover, automating the process of formally assessing conceptual models in this sense (i.e., model validation) is notoriously hard, mainly because the at hand lies in the mind of these stakeholders. In this paper, we provide a novel approach to model validation and automated constraint learning that combines, on one hand, via the visual simulation of that model’s valid instances and, on the other hand, techniques. In our approach, we properly channel the results produced by the application of a visual model finding technique as input to a learning process. We then show how the approach is able to support the modeler in identifying missing constraints from the original model. The approach is validated against a catalog of empirically-elicited conceptual modeling anti-patterns. As we show here, the approach is able to support the automated learning of constraints that are needed to rectify a number of relevant anti-patterns in this catalog.