AN IMPROVED GENETIC ALGORITHM FOR TEST DATA GENERATION FOR SIMULINK MODELS
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DOI:
https://doi.org/10.15625/1813-9663/33/1/9315Keywords:
Genetic algorithm, mutation testing, simulink, test data generationAbstract
Mutation testing is a powerful and e ective software testing technique to assess the quality of test suites. Although many research works have been done in the eld of search-based testing, automatic test data generation based on the mutation analysis method is not straightforward. In this paper, an Improved Genetic Algorithm (IGA) is proposed to increase the quality of test data based on mutation coverage criterion. This algorithm involves some modi cations of genetic operators and the employment of memory mechanism to enhance its e ectiveness. The proposed approach is implemented to generate test data for Simulink models. The obtained results indicated that IGA outperformed the conventional genetic algorithm in terms of the quality of test sets, and the execution time.Metrics
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Published
07-12-2017
How to Cite
[1]
L. T. M. Hanh, N. T. Binh, and K. T. Tung, “AN IMPROVED GENETIC ALGORITHM FOR TEST DATA GENERATION FOR SIMULINK MODELS”, JCC, vol. 33, no. 1, p. 50–69, Dec. 2017.
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Computer Science
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