Abstract

. Software testing is a very important phase in the development of software. Testing includes the generation of test cases which, if done manually, is time consuming. To automate this process and generate optimal test cases, several meta-heuristic techniques have been developed. These approaches include genetic algorithm, cuckoo search, tabu search, intelligent water drop, etc. This paper presents an effective approach for test data generation using the cuckoo search and tabu search algorithms. It combines the cuckoo algorithm's strength of converging to the solution in minimal time along with the tabu mechanism of backtracking from local optima by Lévy flight. The experimental results show that the algorithm is effective in generating test cases optimally and its performance is better than various earlier proposed approaches.