Learning-Based Compositional Model Checking of Behavioral UML Systems
Grumberg Orna, Meller Yael · NATO science for peace and security series. D, Information and communication security · 2016
This work presents a novel approach for applying compositional model checking of behavioral UML models, based on learning. The Unified Modeling Language (UML) is a widely accepted modeling language for embedded and safety critical systems. As such the correct behavior of systems represented as UML models is crucial. Model checking is a successful automated verification technique for checking whether a system satisfies a desired property. However, its applicability is often impeded by its high time and memory requirements. A successful approach to tackle this limitation is compositional model checking. Recently, great advancements have been made in this direction via automatic learning-based Assume-Guarantee reasoning.