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29th Annual International Computer Software and Applications Conference (COMPSAC'05) Volume 2
Edinburgh, Scotland
July 26-July 28
ISBN: 0-7695-2413-3
ASCII Text
x 
 
Xiaoyuan Xie, Baowen Xu, Changhai Nie, Liang Shi, Lei Xu, "Configuration Strategies for Evolutionary Testing," Computer Software and Applications Conference, Annual International, vol. 2, pp. 13-14, 29th Annual International Computer Software and Applications Conference (COMPSAC'05) Volume 2, 2005.
 
 
BibTex
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@article{ 10.1109/COMPSAC.2005.60,
author = {Xiaoyuan Xie and Baowen Xu and Changhai Nie and Liang Shi and Lei Xu},
title = {Configuration Strategies for Evolutionary Testing},
journal ={Computer Software and Applications Conference, Annual International},
volume = {2},
year = {2005},
issn = {0730-3157},
pages = {13-14},
doi = {http://doi.ieeecomputersociety.org/10.1109/COMPSAC.2005.60},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
x 
 
TY - CONF
JO - Computer Software and Applications Conference, Annual International
TI - Configuration Strategies for Evolutionary Testing
SN - 0730-3157
SP13
EP14
A1 - Xiaoyuan Xie,
A1 - Baowen Xu,
A1 - Changhai Nie,
A1 - Liang Shi,
A1 - Lei Xu,
PY - 2005
KW - null
VL - 2
JA - Computer Software and Applications Conference, Annual International
ER -
 
Xiaoyuan Xie, Southeast University and Jiangsu Institute of Software Quality
Baowen Xu, Southeast University, Jiangsu Institute of Software Quality, National University of Defense Technology and Wuhan University
Changhai Nie, Southeast University and Jiangsu Institute of Software Quality
Liang Shi, Southeast University and Jiangsu Institute of Software Quality
Lei Xu, Southeast University and Jiangsu Institute of Software Quality
Evolutionary Testing (ET) is a kind of efficient method of automatically test case generation. ET uses a kind of meta-heuristic search technique, the Genetic Algorithm, to convert the task of test case generation into an optimal problem. Nowadays, ET has been widely researched in many areas, especially in the GA configuration problem. In this paper, we suggest two strategies for the Genetic Algorithm configuration, to improve the performance of ET. One is Annealing Genetic Algorithm (AGA), which alters the mutation probability dynamically, and the other is Restricted Genetic Algorithm (RGA), which adds restrictions into fitness functions. The two strategies made ET hit the global optimal solution in fewer generations, and most offspring genes located in the legal domain.
Citation:
Xiaoyuan Xie, Baowen Xu, Changhai Nie, Liang Shi, Lei Xu, "Configuration Strategies for Evolutionary Testing," compsac, vol. 2, pp.13-14, 29th Annual International Computer Software and Applications Conference (COMPSAC'05) Volume 2, 2005
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