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Intelligence frameworks for assisting modelers in combinatorically challenging domains

Domain-Specific Modeling Languages (DSMLs) are a means of simplifying the development of a large class of systems. There are many domains, however, where the domain constraints are so restrictive and the solution spaces so large that it is extremely difficult for a modeler to manually produce a correct solution using a DSML. For example, modeling the deployment of software components to nodes and observing configuration and resource constraints, even when only a few tens of model entities are present, can easily generate solution spaces with millions or more possibile deployments and few correct ones. This paper address the challenge of creating a Domain-Specific Intelligence Framework (DSIF) to help a modeler solve combinatorically challenging modeling problems. The paper provides five key contributions to the integration of DSMLs and model intelligence. 1) We present experiments showing how a domainspecific knowledge base and domain-specific solution algorithms can greatly reduce the complexity of integrating a solver. 2) We provide methods for templatizing a Knowledge Base (KB) and KB-based constraint solvers so that they can be paramertized by a metamodel. 3) We present experiments validating that a metamodel-parameterized KB and solvers require less code and provide superior performance to generic KBs and solvers. 4) We illustrate methods for using an Observer, templatized by a metamodel, to translate events from the object graph of a DSML tool into DSKB events and DSKB events back to object graph events. 5) Finally, we present a case-study based on a tool for sovling AUTOSAR, an automotive modeling standard, deployment problems.