This article presents and analyzes algorithms that systematically generate random Bayesian networks of varying difficulty levels, with respect to inference using tree clustering. ...
A Random test generator generates executable tests together with their expected results. In the form of a noise-maker, it seeds the program with conditional scheduling primitives ...
This paper considers the problem of maintaining the coverage degree of a wireless sensor network at an application specific level while keeping the sensing units of only a subset o...
This paper proposes a novel method for analyzing large onchip power delivery networks via a stochastic moment matching (SMM) method. The proposed method extends the existing direc...
An important goal of automatic testing techniques, including random testing is to achieve high code coverage with a minimum set of test cases. To meet this goal, random testing res...