Successive Refinement of Models for Model-Based Testing to Increase System Test Effectiveness

Abstract

Model-based testing is used for automatically generating test cases based on models of the system under test. The effectiveness of tests depends on the contents of these models. Therefore, we introduce a novel three-step model refinement approach. We represent test models in the form of Markov chains. First, we update state transition probabilities in these models based on usage profile. Second, we perform an update based on fault likelihood that is estimated with static code analysis. Our third update is based on error likelihood that is estimated with dynamic analysis. We generate and execute test cases after each refinement. We applied our approach for model-based testing of a Smart TV system and new faults were revealed after each refinement.