Deciding bit-vector formulas with mcSAT

Abstract

The Model-Constructing Satisfiability Calculus (mcSAT) is a recently proposed generalization of propositional DPLL/CDCL for reasoning modulo theories. In contrast to most DPLL(T)-based SMT solvers, which carry out conflict-driven learning only on the propositional level, mcSAT calculi can also synthesise new theory literals during learning, resulting in a simple yet very flexible framework for designing efficient decision procedures. We present an mcSAT calculus for the theory of fixed-size bit-vectors, based on tailor-made conflict-driven learning that exploits both propositional and arithmetic properties of bit-vector operations. Our procedure avoids unnecessary bit-blasting and performs well on problems from domains like software verification, and on constraints over large bit-vectors.