Optimum Test Suite Using Fault-Type Coverage-Based Ant Colony Optimization Algorithm

Software Product Lines(SPLs) covers a mixture of features for testing Application Program(SPA). Testing cost reduction is major metric software testing. In combinatorial testing(CT), maximization fault type coverage and test suite plays key role to reduce the SPA. Metaheuristic Genetic Algorithm(GA) do not offer best outcome optimization problem due mutation operation required more computational time. So, Fault-Type Coverage Based Ant Colony Optimization(FTCBACO) algorithm offered in CT. FTCBACO starts with cases assign separate ant each case. Ants elect by updating pheromone trails selection higher probability trails. Best case path least time are taken as optimal solution performing Hence, Technique enriches rate minimizes reducing efficiently