Accelerating Optimal Test Planning With Artificial Neural Networks

In this paper, artificial neural networks for use in an algorithm for determining an optimal test plan are investigated. The neural networks serve to estimate the Probability of Test Success and the expected duration of a reliability test for a product with prior knowledge about the failure distribution. This estimation in negligible time enables the efficient pre-selection of promising test plans. Without such a pre-selection, a comparison of all possible test plans would lead to an unfeasibly large amount of simulation due to the high number of parameters.