Test Case Prioritization for Regression Testing Using Immune Operator

Regression testing is a time consuming, costly process of re-running existing test cases. As software evolves, the regression test suite grows in size. Test case prioritization techniques help by ordering test cases such that at least the test cases which cover the changes made in the software are executed amidst resource and time constraints. Genetic Algorithm (GA) has been widely used for test case prioritization problem, however it has low convergence problem. In this work, the Immune Genetic Algorithm (IGA) is applied for test case prioritization, so that test case prioritization converges earlier. O ur contributions in Immune Prioritization Algorithm (IPA) include a method for vaccine selection, zero drop function and probability selection function. The prioritized result of IPA is evaluated against GA and the statement coverage, decision coverage and block coverage of the test cases prioritized using IPA are found to have improved. Further, IPA showed improved average fitness value as well as optimal fitness value compared to genetic algorithm.