Memory-aware Bounded Model Checking for Linear Hybrid Systems

Bounded Model Checking (BMC) is a successful method for refuting properties of erroneous systems. Initially applied to discrete systems only, BMC could be extended to more complex domains like linear hybrid automata. The increasing complexity coming along with these complex models, but also recent optimizations of SAT-based BMC, like excessive conflict learning, reveal a memory explosion problem especially for deep counterexamples. In this paper we introduce parametric data types for the internal solver structure that, taking advantage of the symmetry of BMC problems, remarkably reduce the memory requirements of the solver.