A genetic algorithm framework for test generation

Test generation using deterministic fault-oriented algorithms is highly complex and time consuming. New approaches are needed to augment the existing techniques, both to reduce execution time and to improve fault coverage. Genetic algorithms (GA's) have been effective in solving many search and optimization problems. Since test generation is a search process over a large vector space, it is an ideal candidate for GA's. In this work, we describe a GA framework for sequential circuit test generation. The GA evolves candidate test vectors and sequences, using a fault simulator to compute the fitness of each candidate test. Various GA parameters are studied, including alphabet size, fitness function, generation gap, population size, and mutation rate, as well as selection and crossover schemes. High fault coverages were obtained for most of the ISCAS'89 sequential benchmark circuits, and execution times were significantly lower than in a deterministic test generator in most cases.