Regular Model Checking Using Solver Technologies and Automata Learning
Regular Model Checking is a popular verification technique where large and even infinite sets of program configurations can be encoded symbolically by finite automata. Thereby, the handling of regular sets of initial and bad configurations often imposes a serious restriction in practical applications. We present two new algorithms both utilizing modern solver technologies and automata learning. The first one works in a CEGAR-like fashion by iteratively refining an abstraction of the reachable state space using counterexamples, while the second one is based on Angluin’s prominent learning algorithm. We show the feasibility and competitiveness of our approaches on different benchmarks and compare them to other established tools.