Genetic Algorithm-Based Test Data Generation for Multiple Paths via Individual Sharing
Joint Authors
Source
Computational Intelligence and Neuroscience
Issue
Vol. 2014, Issue 2014 (31 Dec. 2014), pp.1-12, 12 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2014-10-16
Country of Publication
Egypt
No. of Pages
12
Main Subjects
Abstract EN
The application of genetic algorithms in automatically generating test data has aroused broad concerns and obtained delightful achievements in recent years.
However, the efficiency of genetic algorithm-based test data generation for path testing needs to be further improved.
In this paper, we establish a mathematical model of generating test data for multiple paths coverage.
Then, a multipopulation genetic algorithm with individual sharing is presented to solve the established model.
We not only analyzed the performance of the proposed method theoretically, but also applied it to various programs under test.
The experimental results show that the proposed method can improve the efficiency of generating test data for many paths’ coverage significantly.
American Psychological Association (APA)
Yao, Xiangjuan& Gong, Dunwei. 2014. Genetic Algorithm-Based Test Data Generation for Multiple Paths via Individual Sharing. Computational Intelligence and Neuroscience،Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1016738
Modern Language Association (MLA)
Yao, Xiangjuan& Gong, Dunwei. Genetic Algorithm-Based Test Data Generation for Multiple Paths via Individual Sharing. Computational Intelligence and Neuroscience No. 2014 (2014), pp.1-12.
https://search.emarefa.net/detail/BIM-1016738
American Medical Association (AMA)
Yao, Xiangjuan& Gong, Dunwei. Genetic Algorithm-Based Test Data Generation for Multiple Paths via Individual Sharing. Computational Intelligence and Neuroscience. 2014. Vol. 2014, no. 2014, pp.1-12.
https://search.emarefa.net/detail/BIM-1016738
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1016738