Automated abstraction by incremental refinement in interpolant-based model checking

Abstract—This paper addresses the field of Unbounded Model Checking (UMC) based on SAT engines, where Craig interpolants have recently gained wide acceptance as an automated abstraction technique. We start from the observation that interpolants can be quite effective on large verification instances. As they operate on SAT-generated refutation proofs, interpolants are very good at automatically abstract facts that are not significant for proofs. In this work, we push forward the new idea of generating abstractions without resorting to SAT proofs, and to accept (reject) abstractions whenever they (do not) fulfill given adequacy constraints. We propose an integrated approach smoothly combining the capabilities of interpolation with abstraction and over-approximation techniques, that do not directly derive from SAT refutation proofs. The driving idea of this combination is to incrementally generate, by refinement, an abstract (over-approximate) image, built up from equivalences, implications, ternary and localization abstraction, then (eventually) from SAT refutation proofs. Experimental results, derived from the verification of hard problems, show the robustness of our approach

Partitioning Interpolant-Based Verificationfor effective Unbounded Model Checking

Interpolant-based model checking has been shown to be effective on large verification instances, as it efficiently combines automated abstraction and reachability fixed-point checks. On the other hand, methods based on variable quantification have proved their ability to remove free inputs, thus projecting the search space over state variables. In this paper we propose an integrated approach which combines the abstraction power of interpolation with techniques that rely on AIG and/or BDD representations of states, directly supporting variable quantification and fixed-point checks. The underlying idea of this combination is to adopt AIG- or BDD-based quantifications to limit and restrict the search space and the complexity of the interpolant-based approach. The exploited strategies, most of which are individually well-known, are integrated with a new flavor, specifically designed to improve their effectiveness on difficult verification instances. Experimental results, specifically oriented to hard-to-solve verification problems, show the robustness of our approach