NuMDG: A New Tool for Multiway Decision Graphs Construction

Abstract Multiway Decision Graphs (MDGs) are a canonical representation of a subset of many-sorted first-order logic. This subset generalizes the logic of equality with abstract types and uninterpreted function symbols. The distinction between abstract and concrete sorts mirrors the hardware distinction between data path and control. Here we consider ways to improve MDGs construction. Efficiency is achieved through the use of the Generalized-If-Then-Else (GITE) commonly operator in Binary Decision Diagram packages. Consequently, we review the main algorithms used for MDGs verification techniques. In particular, Relational Product and Pruning by Subsumption are algorithms defined uniformly through this single GITE operator which will lead to a more efficient implementation. Moreover, we provide their correctness proof. This work can be viewed as a way to accommodate the ROBBD algorithms to the realm of abstract sorts and uninterpreted functions. The new tool, called NuMDG, accepts an extended SMV language, supporting abstract data sorts. Finally, we present experimental results demonstrating the efficiency of the NuMDG tool and evaluating its performance using a set of benchmarks from the SMV package. Keywords infinite state model checking, multiway decision graphs, uninterpreted functions