Learning-Based Testing Using SAL (Symbolic Analysis Laboratory) Model Checker

Abstract

This paper studies learning-based testing (LBT) for reactive systems with different learning algorithms and model checkers. LBT is a technique that requires a learning algorithm to learn the models to generate test cases automatically. We have used the generic methodology of LBT to test reactive systems with two different model inference algorithms (i.e., IKL, DKL) and two different model checking tools (i.e., NuSMV, SAL). To investigate the feasibility of LBT, we integrated our SUTs with these algorithms in LBT and tested if LBT optimizes test generation with these algorithms. We tested our SUTs with Boolean data types to check the difference in the working of model inference and model checking algorithms which we analyzed experimentally. The results show that LBT works better with DKL and SAL. DKL is a recently proposed model inference algorithm, and SAL is the latest model checker on which the research is being carried out. DKL and SAL algorithms explore errors in reactive SUTs with the h LBT framework more quickly and efficiently.