| dc.description.abstract | Model-driven engineering (MDE) has been applied extensively to Cyber-physical systems (CPS) in which the functionality emerges from the interactions of computational and physical processes. The CPS design of autonomous vehicles, smart energy systems, public transportation system and Internet of Things are dominantly model based. We combined WebGME, a cloud-based collaborative modeling tool, with FORMULA, a language and tool for specifying and analyzing domain-specific modeling languages, for model-based design, model validation and verification.
However, the lack of a deeper semantic integration between the model engineering framework (GME and currently WebGME) and the FORMULA-based semantic backplane has been one of the major motivations for our research effort. Well-known challenges of introducing model-based engineering to the end-to-end design of CPS product lines in the aerospace and automotive industries are heterogeneity and scalability. To address this issue, this dissertation describes the design and the implementation of a semantic bridge that connects our engineering modeling framework with a graph representation-based modeling framework incorporating graph database, formal semantics, and visualization tools for analysis. The scalability issues are addressed by our incremental version of FORMULA named Differential-FORMULA that can perform efficient model queries and transformations in the face of continual model updates. The novel idea is that we use the same Model-driven engineering methodology to automatically generate a incremental version of the existing model-driven engineering tool by applying model transformation in a bootstrapping way and we compare the benchmarks with other state-of-art modeling tools. | |
