Black-Box Test-Coverage Analysis and Test-Cost Reduction Based on a Bayesian Network Model

Abstract

The growing complexity of circuit boards makes manufacturing test increasingly expensive. In order to reduce test cost, a number of test selection methods have been proposed in the literature. However, only few of these methods can be applied to black-box test-cost reduction. The conventional greedy algorithm, which selects the most important tests by considering both strong and weak relationships among tests, suffers from overfitting. In order to overcome overfitting, we propose a novel black-box test selection method based on a Bayesian network model. First, the problem of reducing black-box test cost is formulated as a constrained optimization problem. Next, a scorebased algorithm is implemented to construct the Bayesian network for black-box tests. Finally, we propose a Bayesian index with the property of Markov blankets, and then an iterative test selection method is developed based on our proposed Bayesian index. The proposed approach ensures that only the strong relationships among black-box tests are used for test selection so that this approach is more robust to overfitting. Two case studies with production test data demonstrate that the proposed approach effectively reduces test cost by up to 14.7%, compared to a conventional greedy algorithm.