3-Valued abstraction: More precision at less cost

AbstractThis paper investigates both the precision and the model checking efficiency of abstract models designed to preserve branching time logics w.r.t. a 3-valued semantics. Current abstract models use ordinary transitions to over approximate the concrete transitions, while they use hyper transitions to under approximate the concrete transitions. In this work, we refer to precision measured w.r.t. the choice of abstract states, independently of the formalism used to describe abstract models. We show that current abstract models do not allow maximal precision. We suggest a new class of models and a construction of an abstract model which is most precise w.r.t. any choice of abstract states. As before, the construction of such models might involve an exponential blowup, which is inherent by the use of hyper transitions. We therefore suggest an efficient algorithm in which the abstract model is constructed during model checking, by need. Our algorithm achieves maximal precision w.r.t. the given property while remaining quadratic in the number of abstract states. To complete the picture, we incorporate it into an abstraction-refinement framework

A Framework for Compositional Verification of Multi-valued Systems via Abstraction-Refinement

We present a framework for fully automated compositional verification of µ-calculus specifications over multi-valued systems, based on multivalued abstraction and refinement. Multi-valued models are widely used in many applications of model checking. They enable a more precise modeling of systems by distinguishing several levels of uncertainty and inconsistency. Successful verification tools such as STE (for hardware) and YASM (for software) are based on multi-valued models. Our compositional approach model checks individual components of a system. Only if all individual checks return indefinite values, the parts of the components which are responsible for these values, are composed and checked. Thus the construction of the full system is avoided. If the latter check is still indefinite, then a refinement is needed. We formalize our framework based on bilattices, consisting of a truth lattice and an information lattice. Formulas interpreted over a multi-valued model are evaluated w.r.t. to the truth lattice. On the other hand, refinement is now aimed at increasing the information level of model details, thus also increasing the information level of the model checking result. Based on the two lattices, we suggest how multi-valued models should be composed, checked, and refined

Systematic Construction of Abstractions for Model-Checking

This paper describes a framework, based on Abstract Interpretation, for creating abstractions for model-checking. Specifically, we study how to abstract models of ¢-calculus and systematically derive abstractions that are constructive, sound, and precise, and apply them to abstracting Kripke structures. The overall approach is based on the use of bilattices to represent partial and inconsistent information.