Deductive reconstruction of MLT* for multi-level modeling

M. Jeusfeld, J. Almeida, V. Carvalho, C. Fonseca, B. Neumayr
Neum20b (2020)
Proceedings of the 23rd ACM/IEEE International Conference on Model Driven Engineering Languages and Systems (MODELS 2020), October 18-23, 2020, Virtual Event, Canada, Companion Proceedings, Article No. 83, ACM Press, DOI:, pp. 83:1-10, 2020.
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Abstract (English)

In the last two decades, about a dozen proposals were made to extend object-oriented modeling by multiple abstraction levels. One group of proposals designates explicit levels to objects and classes. The second group uses the powertype pattern to implicitly establish levels. From this group, we consider two proposals, DeepTelos and MLT*. Both have been defined via axioms and both give a central role to the powertype pattern. In this paper, we reconstruct MLT* with the deductive axiomatization style used for DeepTelos. The resulting specification is executed in a deductive database to check MLT* multi-level models for errors and complete them with derived facts that do not have to be explicitly asserted by modelers. This leverages the rich rules of MLT* with the deductive approach underlying DeepTelos. The effort also allows us to clearly establish the relation between DeepTelos and MLT*, in an attempt to clarify the relations between approaches in this research domain. As a byproduct, we supply MLT-Telos as a fully operational deductive implementation of MLT* to the research community.