Combining model-based and risk-based testing for effective test case generation

Abstract

Model-based testing employs models of the system under test to automatically generate test cases. In this paper, we propose an iterative approach, in which these models are refined based on the principles of risk-based testing. We use Markov Chains as system models, in which transitions among system states are annotated with probabilities. Initially, these probability values are equal and as such, states have equal chances for being visited by the generated test cases. Memory leaks are monitored during the execution of these test cases. Then, transition probabilities are updated based on the risk that a failure can occur due to the observed memory leaks. We applied our approach in the context of an industrial case study for model-based testing of a Smart TV system. We observed promising results, in which several crash failures were detected after an iteration of model refinement. We aim at automating the whole process based on an adaptation model using the history of recorded memory leaks during previous test executions.