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

Auxiliary Variables for BDD-based Representation and Manipulation of Boolean Functions

BDDs are the state-of-the-art technique for representing and manipulating Boolean functions. Their introduction caused a major leap forward in synthesis, verification, and testing. However, they are often unmanageable because of the large amount of nodes. To attack this problem, we insert auxiliary variables that decompose monolithic BDDs in smaller ones. This method works very well for Boolean function representation. As far as combinational circuits are concerned, representing their functions is the main issue. Going into the sequential domain, we focus on traversal techniques. We show that, once we have Boolean functions in decomposed form, symbolic manipulations are viable and efficient. We investigate the relation between auxiliary variables and static and dynamic ordering strategies. Experimental evidence shows that we achieve a certain degree of independence from variable ordering. Thus, this approach can be an alternative to dynamic re-ordering. Experimental results on Boolean function representation, and exact and approximate forward symbolic traversal of FSMs, demonstrate the benefits both in terms of memory requirements and of CPU time.</jats:p