A genetic algorithm for test-suite reduction

In order to reduce the cost of regression testing, researchers have investigated the use of test-suite reduction techniques, which identify a reduced test suite that provides the same coverage of the software according to some criterion as the original test suite. Existing test-suite reduction techniques consider test-case coverage criteria, other criteria such as risk or fault-detection effectiveness, or combinations of these criteria, but ignore the test-execution cost because of the complexity. Firstly, this paper presents a mathematical model for this test-suite reduction problem and transforms it into a linear integer-programming form. Then the paper investigates the use of an evolutionary approach, called genetic algorithm, for this test-suite reduction problem. Unlike other algorithms, our algorithm uses a new criteria, which is a combination of a block based coverage criteria and a test-execution cost criteria, to make decisions about reducing a test suite. Finally, the paper presents the results of the empirical studies of our algorithm. The studies show that our algorithm can significantly reduce the size and the cost of the test-suite for regression testing, and the test-execution cost is one of the most important features that must be taken into consideration for test-suite reduction.