Test Data Generation for Multiple Paths Based on Local Evolution

YAO Xiangjuan; GONG Dunwei; WANG Wenliang

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Article Navigation > Chinese Journal of Electronics > 2015 > 24(1): 46-51

YAO Xiangjuan, GONG Dunwei, WANG Wenliang, “Test Data Generation for Multiple Paths Based on Local Evolution,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 46-51, 2015,

Citation:

YAO Xiangjuan, GONG Dunwei, WANG Wenliang, “Test Data Generation for Multiple Paths Based on Local Evolution,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 46-51, 2015,

YAO Xiangjuan, GONG Dunwei, WANG Wenliang, “Test Data Generation for Multiple Paths Based on Local Evolution,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 46-51, 2015,

Citation:

YAO Xiangjuan, GONG Dunwei, WANG Wenliang, “Test Data Generation for Multiple Paths Based on Local Evolution,” Chinese Journal of Electronics, vol. 24, no. 1, pp. 46-51, 2015,

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Test Data Generation for Multiple Paths Based on Local Evolution

1.
College of Science, China University of Mining and Technology, Xuzhou 221116, China;
2.
School of Information and Electrical Engineering, China University of Mining and Technology, Xuzhou 221116, China

Funds: This work is supported by Natural Science Foundation of China (No.61203304, No.61375067), Natural Science Foundation of Jiangsu Province (No.BK2012566), and the Fundamental Research Funds for the Central Universities (No.2012QNA41).

Received Date: 2012-12-01
Rev Recd Date: 2014-09-01
Publish Date: 2015-01-10

Abstract

Abstract

Generating test data by genetic algorithms is a promising research direction in software testing, among which path coverage is an important test method. The efficiency of test data generation for multi-path coverage needs to be further improved. We propose a test data generation method for multi-path coverage based on a genetic algorithm with local evolution. The mathematical model is established for all target paths, while in the algorithm the individuals are evolved locally according to different objective functions. We can improve the utilization efficiency of test data. The computation cost can be reduced by using fitness functions of different granularity in different phases of the algorithm.
- Test data generation,
- Path coverage,
- Genetic algorithm,
- Local evolution

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References(16)

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