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 and-inverter graph (AIG) and/or binary decision diagram (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.

Constraints represent a key component of state-of-the-art verification tools based on compositional approaches and assume-guarantee reasoning. In recent years, most of the research efforts on verification constraints have focused on defining formats and techniques to encode, or to synthesize, constraints starting from the specification of the design. In this paper, we analyze the impact of constraints on the performance of model checking tools, and we discuss how to effectively exploit them. We also introduce an approach to explicitly derive verification constraints hidden in the design and/or in the property under verification. Such constraints may simply come from true design constraints, embedded within the properties, or may be generated in the general effort to reduce or partition the state space. Experimental results show that, in both cases, we can reap benefits for the overall verification process in several hard-to-solve designs, where we obtain speed-ups of more than one order of magnitude.

Interpolant-based model checking has been shown effective on large verification instances, as it efficiently combines automated abstraction and 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 combining the abstraction power of interpolation with techniques relying on AIG and/or BDD representations of states, 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, individually well-known, are integrated with a new flavor, specifically designed to improve their effectiveness on large verification instances. Experimental results, oriented to hard-to-solve verification problems, show the robustness of our approach.