Probabilistic networks for verifying automated testing of high speed telecommunication equipment through the development lifecycle

Learning probabilistic networks for expert system applications has seen a great surge of research activity in recent years. The paper reports on the adoption of probabilistic networks for verifying the pass/fail result of automated testing at the software verification stage in the development lifecycle of high-speed telecommunications equipment. The focus is on learning Bayesian belief networks (BBNs) from automated test data on a per test execution basis. This facilitates result classification and assurance, taking advantage of their graphical nature to provide accountability for the decision.