An Novel Testing Sequence Optimization Method under Dynamic Environments

Over decades, testing sequence optimization has been an important part for the design of testability (DFT). However, most existed method suffers from high searching complexity. Meanwhile, with the high speed of updating with electronic technology, the cost for test points and the probability of the fault state varies due to the development of the systems. Therefore, how to update the testing sequential model comes to be important to improve the efficiency on diagnosis. Since that, in this paper, we proposed a new testing sequence optimization method based on AO* and dynamic programming to improve the efficiency on generate solutions. Also, we develop the updating method for the problem under three dynamic conditions which may change the optima solution. The experiments shows that our method can achieve better performance than several state-of-art algorithms.