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 AN IMPROVED GENETIC ALGORITHM FOR TEST DATA GENERATION FOR SIMULINK MODEL
Tác giả hoặc Nhóm tác giả: LE THI MY HANH, NGUYEN THANH BINH, KHUAT THANH TUNG
Nơi đăng: Journal of Computer Science and Cybernetics; Số: V.33, N.1 (2017);Từ->đến trang: 50-69;Năm: 2017
Lĩnh vực: Công nghệ thông tin; Loại: Bài báo khoa học; Thể loại: Trong nước
TÓM TẮT
Mutation testing is a powerful and effective software testing technique to assess the quality of test suites. Although many research works have been done in the field 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 modifications of genetic operators and the employment of memory mechanism to enhance its effectiveness. 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.
ABSTRACT
Mutation testing is a powerful and effective software testing technique to assess the quality of test suites. Although many research works have been done in the field 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 modifications of genetic operators and the employment of memory mechanism to enhance its effectiveness. 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.
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