Test-Suite Minimization Using Genetic Algorithms
用遗传算法的测试用例最小化

Regression testing is an expensive process used to revalidate the modified program. As the software is modified and new test cases are added to the test suite, the test suite grows and the cost of regression testing increases. Regression test-suite minimization techniques attempt to reduce the cost of regression testing by identifying a minimized test-suite 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 minimization. The algorithm designs the gene codes of the individuals and builds the initial population based on the test history, calculates the fitness value of each individual 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. Finally, some results of studies of this minimization algorithm are presented. The results show that, genetic algorithms can significantly reduce the size and the cost of the test-suite for regression testing, and achieves good cost-effectiveness.