Bayesian networks based testability prediction of electronic equipment

The complexity of modern electronic equipment is putting new demand on system testability. Well design for testability (DFT) can save cost in fault detection and isolation, promote efficiency of system maintenance. The primary goal of testability prediction is to analyze and evaluate testability figures of merit (TFOMs) of unit under test (UUT) to support the assessment of the quality of DFT. Bayesian networks (BNs) are the combination of probability theory and graph theory, which has exhibited distinguished performance in representation and reasoning of uncertainty knowledge. So we combine BNs and testability prediction project together. The testability prediction method based on BNs can not only be modeled conveniently, and easy to be integrated into information framework of testability engineering. Predicted result from Bayesian method is more believable than traditional methods.