Multi-objective Test Case Prioritization Using Improved Pareto-Optimal Clonal Selection Algorithm

Regression test plays a vital role in software testing by ensuring the quality and stability of the developed software. During regression test a large number of test cases are involved thus making the process expensive and difficult. In order to reduce the cost and time of regression test, test case prioritization is applied. However in real world scenario multiple testing criteria and constraint are evolved, such as to detect all faults within minimum time, and to detect most severe faults earlier. This takes the test case prioritization problem turn into multi-objective test case prioritization paradigm. In this paper an improved pareto-optimal clonal selection algorithm is proposed to generate test case order depending on three objective such as minimum execution time, maximum severity fault identification and cost-cognizant average percentage of fault detected. The experimental analysis is conducted over an industrial project with seven different versions for which the proposed approach generates scheduled test case order. And it is concluded that the performance of proposed approach is better than other tested algorithms like random approach, weighted genetic algorithm, greedy and NSGA-II.