Increasing test efficiency by risk-driven model-based testing

Abstract

We introduce an approach and a tool, RIMA, for adapting test models used for model-based testing to augment information regarding failure risk. We represent test models in the form of Markov chains. These models comprise a set of states and a set of state transitions that are annotated with probability values. These values steer the test case generation process, which aims at covering the most probable paths. RIMA refines these models in 3 steps. First, it updates transition probabilities based on a collected usage profile. Second, it updates the resulting models based on fault likelihood at each state, which is estimated based on static code analysis. Third, it performs updates based on error likelihood at each state, which is estimated with dynamic analysis. The approach is evaluated with two industrial case studies for testing digital TVs and smart phones. Results show that the approach increases test efficiency by revealing more faults in less testing time.