Statistical Model Checking : An Overview

TL;DR

This paper surveys the statistical model checking approach, which uses simulation and hypothesis testing to verify stochastic system properties efficiently and simply, contrasting with traditional numerical methods.

Contribution

It provides an overview of statistical model checking, highlighting its advantages over numerical approaches in terms of efficiency, uniformity, and simplicity.

Findings

Statistical model checking offers a more efficient verification method.

It simplifies the process compared to numerical algorithms.

The approach is versatile across different stochastic systems.

Abstract

Quantitative properties of stochastic systems are usually specified in logics that allow one to compare the measure of executions satisfying certain temporal properties with thresholds. The model checking problem for stochastic systems with respect to such logics is typically solved by a numerical approach that iteratively computes (or approximates) the exact measure of paths satisfying relevant subformulas; the algorithms themselves depend on the class of systems being analyzed as well as the logic used for specifying the properties. Another approach to solve the model checking problem is to \emph{simulate} the system for finitely many runs, and use \emph{hypothesis testing} to infer whether the samples provide a \emph{statistical} evidence for the satisfaction or violation of the specification. In this short paper, we survey the statistical approach, and outline its main advantages in terms of efficiency, uniformity, and simplicity.