A hybrid test optimization framework using memetic algorithm with cuckoo flocking based search approach

The testing process of industrial strength applications usually takes more time to ensure that all the components are rigorously tested to have failure-free operation upon delivery. This research work proposed a hybrid optimization approach that combines the population based multi-objective optimization approach namely Memetic Algorithm with Cuckoo Search (MA-CK) to generate optimal number of test cases that achieves the specified test adequacy criteria based on mutation score and branch coverage. Further, GA, HGA and MA based heuristic algorithms are empirically evaluated and it has been shown that the proposed MA with cuckoo search based optimization algorithm provides an optimal solution.