Test Case Generation Based on Hierarchical Genetic Algorithm

DOI

10.2991/mce-14.2014.61How to use a DOI?

Keywords

hierarchical genetic algorithm; test case; son population; local convergence; benchmark program

Abstract

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.

Copyright

© 2014, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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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  - Proceedings of the 2014 International Conference on Mechatronics, Control and Electronic Engineering
PB  - Atlantis Press
SP  - 278
EP  - 281
SN  - 1951-6851
UR  - https://doi.org/10.2991/mce-14.2014.61
DO  - 10.2991/mce-14.2014.61
ID  - Liu2014/03
ER  -