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Test data generation for path coverage of message-passing parallel programs based on co-evolutionary genetic algorithms.

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Název: Test data generation for path coverage of message-passing parallel programs based on co-evolutionary genetic algorithms.
Autoři: Tian, Tian, Gong, Dunwei
Zdroj: Automated Software Engineering; Sep2016, Vol. 23 Issue 3, p469-500, 32p
Témata: GENETIC algorithms, COMPUTER software testing, MATHEMATICAL optimization, COMPUTER programming, EVOLUTIONARY algorithms
Abstrakt: Employing genetic algorithms to generate test data for path coverage has been an important method in software testing. Previous work, however, is suitable mainly for serial programs. Automatic test data generation for path coverage of message-passing parallel programs without non-determinacy is investigated in this study by using co-evolutionary genetic algorithms. This problem is first formulated as a single-objective optimization problem, and then a novel co-evolutionary genetic algorithm is proposed to tackle the formulated optimization problem. This method employs the alternate co-evolution of two kinds of populations to generate test data that meet path coverage. The proposed method is applied to seven parallel programs, and compared with the other three methods. The experimental results show that the proposed method has the best success rate and the least number of evaluated individuals and time consumption. [ABSTRACT FROM AUTHOR]
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Databáze: Complementary Index
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Abstrakt:Employing genetic algorithms to generate test data for path coverage has been an important method in software testing. Previous work, however, is suitable mainly for serial programs. Automatic test data generation for path coverage of message-passing parallel programs without non-determinacy is investigated in this study by using co-evolutionary genetic algorithms. This problem is first formulated as a single-objective optimization problem, and then a novel co-evolutionary genetic algorithm is proposed to tackle the formulated optimization problem. This method employs the alternate co-evolution of two kinds of populations to generate test data that meet path coverage. The proposed method is applied to seven parallel programs, and compared with the other three methods. The experimental results show that the proposed method has the best success rate and the least number of evaluated individuals and time consumption. [ABSTRACT FROM AUTHOR]
ISSN:09288910
DOI:10.1007/s10515-014-0173-z