A Tuned Version of Ant Colony Optimization Algorithm (TACO) for Uniform Strength T-way Test Suite Generator: An Execution’s Time Comparison

Abstract

Abstract Software testing is one of important phase in software development. The capabilities of t-way testing to cater bugs due to interactions while reducing the test suite size compare to exhaustive testing has been proven in past decades. However, the execution’s time of the t-way strategy also should be given attention as it could increase the productivity of the testing phase. Thus, this paper proposed a tune version of ant colony optimization algorithm (TACO). TACO is metaheuristic strategy where it adopts ant colony optimization in generating test suites. As further improvement, TACO also integrated with fuzzy logic to dynamically select amount of ant in the algorithm. TACO able to supports uniform strength t-way testing. Experiment result shows that TACO produce a remarkable result of test suite size and execution’s time compared to other strategy for uniform strength t-way testing.