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 focused 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 proposed model builds the initial population based on test history, calculates the fitness value using coverage and run time of test case, and then selectively breeds the successive generations using genetic operations and allows only the fit tests to the reduced suite. This generational process is repeated until an optimized test-suite is found.
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