Test Case Generation Based on Hierarchical Genetic Algorithm
Shurong Liu, Jingfeng Xue, Changzhen Hu, Zhiqiang Li
Available Online March 2014.
- https://doi.org/10.2991/mce-14.2014.61How to use a DOI?
- hierarchical genetic algorithm; test case; son population; local convergence; benchmark program
- The basic genetic algorithm was proposed to optimize the test case generation. It has been applied widely. Based on basic genetic algorithm, this paper proposed the hierarchical genetic algorithm to generate test cases. The hierarchical genetic algorithm divided the initial population into hierarchical son population and operated selection, crossover and mutation among son population independently. In the hierarchical genetic algorithm, the evolution of population was firstly operated between all layers, if the algorithm can't get the best test cases, it entered the next generation. Using this mechanism, the hierarchical genetic algorithm can avoid effectively ‘inbreeding’, ‘local convergence’, ‘slow convergence’ phenomenon. So it was the better way to generate test cases. This paper did the experiment using 3 benchmark program: triangle classification, bubble sort, the Max and Min. The experimental results show that the quality of test cases and the efficiency of generating test cases are improved markedly by hierarchical genetic algorithm compared with the basic genetic algorithm.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - CONF AU - Shurong Liu AU - Jingfeng Xue AU - Changzhen Hu AU - Zhiqiang Li PY - 2014/03 DA - 2014/03 TI - Test Case Generation Based on Hierarchical Genetic Algorithm BT - 2014 International Conference on Mechatronics, Control and Electronic Engineering (MCE-14) PB - Atlantis Press SP - 278 EP - 281 SN - 1951-6851 UR - https://doi.org/10.2991/mce-14.2014.61 DO - https://doi.org/10.2991/mce-14.2014.61 ID - Liu2014/03 ER -