Model integration by hybrid model virtualization

Abstract

Multiple teams working on a single system may each have different viewpoints, and thus, use different models. These models may have partly shared, unique, or interrelated information, requiring model integration. To work faster and in a more parallel way, temporary inconsistencies between multiple models may be accepted. However, shared information only edited by a single team could still be immediately made known globally. The two main approaches to model integration are model virtualization, i.e., deriving all models from a single source of truth and model synchronization, i.e., propagating changes between different materialized models. While model virtualization does not allow temporary inconsistencies between models, model synchronization may require storing duplicate information redundantly, even if only a single team is involved. Thus, this thesis combines model virtualization with model synchronization into a hybrid approach. A new model virtualization approach helps arbitrarily adding or subtracting models from a base model. The base model can be a single model, an intersection or union of multiple models, a modification of another base model, or a model derivation. As we can store arbitrary (user) changes to the base model without affecting it, we allow temporary inconsistencies and arbitrary changes to the base model, e.g., as a result of changing the derivations source model. Incompatible changes never require user intervention, but just cause semantic constraint violations in a newly defined synchronization model, which is valid if and only if all inter-model constraints including feature derivations are fulfilled. To produce quickfix suggestions in (textual) model editors, optimal model synchronization is regarded as finding an optimal synchronization model. For this optimization, both model finders and heuristic search is employed. Model derivations can be specified using a new basic model derivation language, which includes both derivation and synchronization constraints in a single model. This allows for pure derivation by not editing the derived model as well as pure synchronization by specifying constraints just for inter-model consistency, but not for derivation. This hybrid approach is feasible and can support use cases like editing multiple models simultaneously using virtualization. Our proposed model repair does significantly reduce the number of (synchronization) constraint violations and prevent new ones due to improved autocompletion as shown in our evaluation scenarios.