Epistatic Genetic Algorithm for Test Case Prioritization

Search based technologies have been widely used in regression test suite optimization, including test case prioritization, test case selection and test suite minimization, to improve the efficiency and reduce the cost of testing. Unlike test case selection and test suite minimization, the evaluation of test case prioritization is based on the test case execution sequence, in which genetic algorithm is one of the most popular algorithms employed. When permutation encoding is used to represent the execution sequence, the execution of previous test cases can affect the presence of the following test cases, namely epistatic effect. In this paper, the application of epistatic domains theory in genetic algorithms for test case prioritization is analyzed, where Epistatic Test Case Segment is defined. Two associated crossover operators are proposed based on epistasis. The empirical studies show that the proposed two-point crossover operator, E-Ord, outperform the crossover PMX, and can produce higher fitness with a faster convergence.

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