Aliasing probabilities for feedback signature compression of test data

A computationally efficient Markov state space model is developed for determining the aliasing probability of a linear feedback shift register when used for test data reduction. The model studied can be used to test data errors which have a constant of probability of error, correlated or repeated use errors, or time varying error probability. Based on a number of simulations of various error models and feedback polynomials it appears that a primitive polynomial, with about half its terms nonzero, has the best dynamic performance in most cases