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

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.

Trading Accuracy for Performance in Data Processing Applications.

“…Although our approach is in the context of these works, we specially focus on providing the necessary mechanisms for obtaining the accuracy of the results when applying different types of approximations. Regarding random approximations, other authors use sampling-based approaches to improve the execution time in large models [BHK18]. However, their proposal is limited to models with up to 20,853 elements, clearly insufficient for the kinds of models our proposal targets, with more than 2 millions of elements.…”