An Approximation-based Approach for the Random Exploration of Large Models

TL;DR

This paper introduces an approximation-based method to enable the application of random exploration techniques to large models, improving scalability and efficiency in model-based testing.

Contribution

It presents a novel approach using statistical approximations to extend random exploration methods to large models, which was previously infeasible.

Findings

Significant reduction in computation time for large models

Improved quality of generated test suites

Successful application to models of communicating protocols

Abstract

System modeling is a classical approach to ensure their reliability since it is suitable both for a formal verification and for software testing techniques. In the context of model-based testing an approach combining random testing and coverage based testing has been recently introduced [9]. However, this approach is not tractable on quite large models. In this paper we show how to use statistical approximations to make the approach work on larger models. Experimental results, on models of communicating protocols, are provided; they are very promising, both for the computation time and for the quality of the generated test suites.