An enhanced Whale Optimisation Algorithm for test case suite selection: Ambha_WOA

Abstract

Background: In the software industry, it is a challenging task to meet the everchanging needs of the user and set harmony of software modifications with customers’ expectations. Retesting of modified software using regression testing techniques provides confidence that newly added modules have not affected on reliability and normal functioning of the software. Aim: In regression testing, test case suite selection techniques aim to find a subset of selected test cases that maximize fault revealing capability and reduce test case suite size as well as execution time. We aim to develop an enhanced metaheuristic algorithm based on the whale optimization algorithm for test case selection problem. Method: Whales are used as search agents to determine selected test case suite with the objective of finding the global optimal solution for TCS problems. Results: Ambha_WOA algorithm is implemented on 12 test case suites taken from GitHub and SIR repositories.Performance metrics – APFD metric, classification accuracy, Fault detection ratio metric, precision and recall are considered to compare proposed algorithm with classical whale optimization algorithm. To validate results, NP-statistical tests (Wilcoxon sign rank and F test) are conducted.Conclusion: Metaheuristic algorithms are efficient to deal with TCS problems in regression testing by giving an optimal solution.