Detecting Spurious Counterexamples Efficiently in Abstract Model Checking

TL;DR

This paper introduces efficient algorithms to detect spurious counterexamples in abstract model checking, improving the abstraction-refinement process by accurately identifying false counterexamples.

Contribution

It provides a formal definition of spurious paths and proposes algorithms that enhance the efficiency of spurious counterexample detection in model checking.

Findings

Algorithms significantly reduce detection time

Improves accuracy of abstraction-refinement loop

Formalizes spurious path concept

Abstract

Abstraction is one of the most important strategies for dealing with the state space explosion problem in model checking. In the abstract model, the state space is largely reduced, however, a counterexample found in such a model may not be a real counterexample in the concrete model. Accordingly, the abstract model needs to be further refined. How to check whether or not a reported counterexample is spurious is a key problem in the abstraction-refinement loop. In this paper, a formal definition for spurious path is given. Based on it, efficient algorithms for detecting spurious counterexamples are proposed.