White-Box Test Case Generation Based on Improved Genetic Algorithm
Some intermittent or transient failures are particularly difficult to diagnose in highly complex and interconnected systems. This paper focuses on the use of genetic algorithms for automatically generating software test cases. In particular, this research extends a newly improved genetic algorithm, which adopts back propagation algorithm for local fine-tuning in the final link, and speeds up access to the best population. The various approaches offer opportunities for performance improvements that make these techniques more scalable for realistic applications.
KeywordsGenetic Algorithm Fitness Function Test Case Generation Improve Genetic Algorithm Group Optimization Search
Unable to display preview. Download preview PDF.
- 1.Tsoulos, I.G., Lagaris, I.E., Gen, M.: An Enhanced Genetic Algorithm for Global Optimization. Computer Physics Communications 61(19), 2925–2936 (2008)Google Scholar
- 3.Berndt, D.J., Watkins, A.: Investigating the performance of genetic algorithm-based software test case generation. High Assurance Systems Engineering 4(13), 261–262 (2004)Google Scholar
- 4.Berndt, D., Fisher, J., Johnson, L.: Breeding software test cases with genetic algorithm. System Sciences 02(01), 6–9 (2003)Google Scholar
- 5.Yao, Y.: New test case generation method based on genetic algorithm. Computer & Digital Engineering 231(1), 18–21 (2009)Google Scholar
- 7.Li, K.-S., Dai, Z.-H.: An improved BP algorithm based on evolutionary algorithm. Microcomputer Information, 23–25 (2010)Google Scholar
- 9.Du, S.-Q.: A novel algorithm of optimizing neural network weights and its application. Journal of Northwest University for Nationalities 30(76), 27–31 (2009)Google Scholar