Model Checking with Quantified Boolean Formulas

Modern symbolic model checking techniques use Binary Decision Diagrams (BDD) and propositional satisfiability (SAT) decision procedures for checking validity and satisfiability of propositional Boolean formulas, which are used to encode sub-problems of symbolic model checking. Usage of propositional formulas imposes a potential exponential memory blow-up on the model checking algorithms due to the big formula sizes. Model checking methods based on the validity of Quantified Boolean Formulas (QBF) allow an exponentially more succinct representation of the checked formulas, but have not been widely used, because of the lack of an efficient decision procedure for QBF. In this work, an evaluation of the usage of QBF in bounded model checking (BMC) is presented, using general-purpose SAT and QBF solvers. Additionally, a special-purpose decision procedure for QBF used in BMC is developed, and compared with the methods using general-purpose SAT and QBF solvers on real-life industrial benchmarks.