Test Selection Policies for Faster Incremental Fault Detection

Incremental Automatic Functional Fault Detective is an incremental methodology based on a Bayesian Belief Network for the identification of the faulty component in a complex system, using data collected from a test session. Incremental Automatic Functional Fault Detective reduces time, cost and efforts during the diagnostic phase by implementing a step-by-step selection of the tests to be executed from the set of available tests. This paper focuses on the evolution of the Bayesian Belief Network nodes probabilities, presenting some selection heuristics to reduce the number of required tests. Validation is performed on a set of experimental results.