Effectively generating random test data via cellular automata

Testing is a costly process that is critical for assessing system behavior. Random testing is a widely used testing method. Generations of random test data are related to choices of random number generators. Whenever test cases are generated, a random number generator is chosen. This paper investigates the use of cellular automata, a generic algorithm, in the generation of test data. Based on knowledge of the geometric shape of the input domain and failure regions, we present methods for effective generation of test data by using cellular automata. In addition, we demonstrate by examples that cellular automata give an alternative method for generating test data in high dimensions.