An Optimization Strategy for Evolutionary Testing Based on Cataclysm

Evolutionary Testing (ET) is an effective test case generation technique which uses some meta-heuristic search algorithm, especially genetic algorithm, to generate test cases automatically. However, the prematurity of the population may decrease the performance of ET. To solve this problem, this paper presents a novel optimization strategy based on cataclysm. It monitors the diversity of population during the evolution process of ET. Once the prematurity is detected, it will use the operator, cataclysm, to recover the diversity of the population. The experimental results show that the proposed strategy can improve the performance of ET evidently.