Improving Bounded Model Checking Exploiting Interpolation-Based Learning and Strengthening

Bounded Model Checking (BMC) is one of the most prominent approaches used as a falsification engine, capable of identifying counterexamples of bounded length, in a scalable and sustainable way. Nevertheless, in the context of a portfolio-based verification suite, BMC can benefit from potential interaction with other engines, exploiting their capabilities and partial results as a form of application-dependant learning. In the past, previous works tackled the issue of using over-approximated state sets generated via Binary Decision Diagrams (BDD) based traversals. In a sense, BDD engines can be considered as external tools, whereas interpolants are directly related to BMC problems. Since interpolants come from Boolean satisfiability (SAT) refutation proofs, their role as a SAT-based learning can be potentially higher. Furthermore, their integration is more tightly linked to the BMC problem at hand. In this paper we aim at improving the efficiency of SAT calls in BMC problems, exploiting interpolation-based invariants computed over different cut points, as additional constraints for BMC problems. We experimentally evaluate costs and benefits of our proposed approach on a set of publicly available model checking problems.