摘要
Evolutionary testing (ET) is an effective test case generation technique which uses some meta-heuristic search algorithm, especially genetic algorithm, to generate test case automatically. However, the population prematurity problem may decrease the performance of ET. In this paper, a hybrid optimization strategy is proposed based on extended cataclysm which integrates both static configuration strategies and dynamic optimization strategy. Dynamic optimization strategy included the optimization of initial population and the dynamic population optimization based on extended cataclysm, where the diversity of population was monitored during the evolution process of ET, and once the population prematurity was detected, extended cataclysm operation was used to renew the diversity of the population. Experimental results show that the hybrid optimization strategy can improve the performance of ET.
出处
《国际计算机前沿大会会议论文集》
2019年第2期51-53,共3页
International Conference of Pioneering Computer Scientists, Engineers and Educators（ICPCSEE）
基金
National Natural Science Foundation of China under Grant No. 61806068, 61672204
by Visiting Scholar at Home and Aboard Funded Project of Universities of Anhui Province under Grant gxfxZD2016209
by Key Technologies R&D Program of Anhui Province under Grant 1804a09020058
by the Major Program for Scientific and Technological of Anhui Province under Grant 17030901026
by Talent Research Foundation Project of Hefei University under Grant 16-17RC23
by Humanities and Social Science Research Project of Universities of Anhui Province under Grant SK2018A0605.
