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Application of Genetic Algorithms in Software Testing
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Author(s): Baowen Xu (Southeast University & Jiangsu Institute of Software Quality, China), Xiaoyuan Xie (Southeast University & Jiangsu Institute of Software Quality, China), Liang Shi (Southeast University & Jiangsu Institute of Software Quality, China)and Changhai Nie (Southeast University & Jiangsu Institute of Software Quality, China)
Copyright: 2007
Pages: 31
Source title:
Advances in Machine Learning Applications in Software Engineering
Source Author(s)/Editor(s): Du Zhang (California State University, USA)and Jeffery J.P. Tsai (University of Illinois at Chicago, USA)
DOI: 10.4018/978-1-59140-941-1.ch012
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Abstract
Genetic algorithms are a kind of global meta-heuristic search technique that searches intelligently for optimal solutions to a problem. Evolutionary testing is a promise testing technique, which utilises genetic algorithms to generate test data for various testing objectives. It has been researched and applied in many testing areas, including structural testing, temporal performance testing, safety testing, specification-based testing, and so forth. Experimental studies have shown that compared with the traditional techniques, evolutionary testing can greatly improve the testing efficiency.
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