Proof-Guided Underapproximation Widening for Bounded Model Checking

Abstract

AbstractBounded Model Checking (BMC) is a popularly used strategy for program verification and it has been explored extensively over the past decade. Despite such a long history, BMC still faces scalability challenges as programs continue to grow larger and more complex. One approach that has proven to be effective in verifying large programs is called Counterexample Guided Abstraction Refinement (CEGAR). In this work, we propose a complementary approach to CEGAR for bounded model checking of sequential programs: in contrast to CEGAR, our algorithm gradually widens underapproximations of a program, guided by the proofs of unsatisfiability. We implemented our ideas in a tool called Legion. We compare the performance of Legion against that of Corral, a state-of-the-art verifier from Microsoft, that utilizes the CEGAR strategy. We conduct our experiments on 727 Windows and Linux device driver benchmarks. We find that Legion is able to solve 12% more instances than Corral and that Legion exhibits a complementary behavior to that of Corral. Motivated by this, we also build a portfolio verifier, $$\textsc {Legion}^{+}$$ L E G I O N + , that attempts to draw the best of Legion and Corral. Our portfolio, $$\textsc {Legion}^{+}$$ L E G I O N + , solves 15% more benchmarks than Corral with similar computational resource constraints (i.e. each verifier in the portfolio is run with a time budget that is half of the time budget of Corral). Moreover, it is found to be $$2.9\times $$ 2.9 × faster than Corral on benchmarks that are solved by both Corral and $$\textsc {Legion}^{+}$$ L E G I O N + .