ABSTRACT
Search based testing is the new emerging arena of software testing, where nature inspired metaheuristic algorithms are used to carefully explored the entire solution space, using the exploration or exploitation capability of those metaheuristics, selecting the best suitable solutions i.e. the test input data, targeting specific coverage criteria. After successful application of many metaheuristic algorithms like GA, PSO, CO, ACO and Firefly etc., the hybrid metaheuristics are gradually being adopted to the same area of search based software testing. In this paper a metaheuristic hybrid algorithm, PSO-GSA has been applied to generate test suites for testing object oriented programs, fulfilling transition path coverage criteria. The main objective here is to combine the best features of PSO i. e. exploitation with the good exploration capabilities of GSA that will enhance their combined performances thus generating good quality solutions. Some bench mark problems are used and the outcomes are analysed with the outcomes of another two hybrid algorithms, hybrid Firefly (FA-DE) and hybrid Cuckoo search optimization (CS- SA) algorithms. The results revealed that the performance of PSO-GSA is quite promising showing faster convergence and better coverage.
