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Erschienen in:
2005 | OriginalPaper | Buchkapitel
Test-Suite Reduction Using Genetic Algorithm
verfasst von : Xue-ying Ma, Bin-kui Sheng, Cheng-qing Ye
Erschienen in: Advanced Parallel Processing Technologies
Verlag: Springer Berlin Heidelberg
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As the software is modified and new test cases are added to the test-suite, the size of the test-suite grows and the cost of regression testing increases. In order to decrease the cost of regression testing, researchers have researched on the use of test-suite reduction techniques, which identify a subset of test cases that provides the same coverage of the software, according to some criterion, as the original test-suite. This paper investigates the use of an evolutionary approach, called genetic algorithms, for test-suite reduction. The algorithm builds the initial population based on test history, calculates the fitness value using coverage and cost information, and then selectively breeds the successive generations using genetic operations. This generational process is repeated until a minimized test-suite is founded. The results of studies show that, genetic algorithms can significantly reduce the size of the test-suite and the cost of regression testing, and achieves good cost-effectiveness.