Incremental Backward Change Propagation of View Models by Logic Solvers

View models are key concepts of domain-specific modeling to provide task-specific focus (e.g., power or communication architecture of a system) to the designers by highlighting only the relevant aspects of the system. View models can be specified by unidirectional forward transformations (frequently captured by graph queries), and automatically maintained upon changes of the underlying source model using incremental transformation techniques. However, tracing back complex changes from one or more abstract view to the underlying source model is a challenging task, which, in general, requires the simultaneous analysis of transformation specifications and well-formedness constraints to create valid changes in the source model. In this paper we introduce a novel delta-based backward transformation technique using SAT solvers to synthetize valid and consistent change candidates in the source model, where only forward transformation rules are specified for the view models

Graph Consistency as a Graduated Property

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Finding Achievable Features and Constraint Conflicts for Inconsistent Metamodels

Determining the consistency of a metamodel is a task of generating a metamodel instance that not only meets structural constraints but also constraints written in Object Constraint Language (OCL). Those constraints can be conflicting, resulting in inconsistencies. When this happens, the existing techniques and tools have no knowledge about which constraints are achievable and which ones cause the conflicts. In this paper, we present an approach to finding achievable metamodel features and constraint conflicts for inconsistent metamodels. This approach allows users to rank individual metamodel features and works by reducing it to a weighted maximum satisfiability modulo theories (MaxSMT). This reduction allows us to utilise SMT solvers to tackle multiple ranked constraints and at the same time locate conflicts among them. We have prototyped this approach, incorporated it into an existing modelling tool, and evaluated it against a benchmark. The preliminary results show that our approach is promising and scalable