Black-Box Test-Cost Reduction Based on Bayesian Network Models

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. In this article, we propose a novel black-box test selection method based on Bayesian networks (BNs), which extract the strong relationship among tests. First, the problem of reducing the black-box test cost is formulated as a constrained optimization problem. Next, multiple structure learning and transfer learning algorithms are implemented to construct BN models. Based on these BN models, we propose an iterative test selection method with a new metric, Bayesian index, for test-cost reduction. In addition, averaging strategies are applied to enhance the reduction performance. Finally, a robust model selection framework is proposed to select the optimal BN model for test-cost reduction. Two case studies with production test data demonstrate that when no prior information is provided, our proposed approach effectively reduces the test cost by up to 14.7%, compared to the state-of-the-art greedy algorithm. Moreover, our proposed approach further reduces the test cost by up to 7.1% when prior information is provided from similar products.