Deep Reactions: Realizing and Evaluating Consistency Preservation for Multi-Level, Multi-View Models

Abstract

In view-based development, consistency plays a central role across concrete models. Consistency can be achieved automatically through consistency-preservation rules defined, for instance, in a change-driven language such as the Reactions language of the Vitruvius framework. This language, however, assumes the classical two-level architecture with a strict separation of type and instance level and can therefore only handle multi-level models through their generic linguistic structure. This forces domain questions to be encoded as not statically typed string operations on infrastructure metaclasses. Based on a previously published vision, this paper presents the Deep Reactions language. It lets a stable region of a deep model serve as an ontological metamodel, over which ontological types and features can be referenced directly and deep characterization through offspring-aware triggers can be exploited. Building on the stable code generation of the original Reactions language, a pre-processing step first transforms Deep Reactions into regular Reactions which ensures compatibility with existing implementations. Our evaluation shows that the number of referenced metamodels and the token count can be reduced while removing 83% of statically unvalidated type references.