A HYBRID TEST OPTIMIZATION FRAMEWORK - COUPLING GENETIC ALGORITHM WITH LOCAL SEARCH TECHNIQUE

Quality of test, cases is determined by then ability to uncover as many micas as possible in the software code In our approach, we applied Hybrid Genetic Algorithm (HGA) for improving the quality of test cases This improvement can be achieved by analyzing both mutation score and path coverage of each test case Our approach selects effective test cases that ha e higher mutation score and path coverage how a near infinite number of test cases Hence, the final test set size is reduced which in turn reduces the total tame needed in testing activity In our proposed framework, we included two improvement heuristics namely RemoveTop and LocalBest. to achieve near global optimal solution Finally. we compared the efficiency of the Lest, cases generated by out approach against the existing test ease optimization approaches such as Simple Genetic Algorithm (SGA) and Bacteriologic Algorithm (BA) and concluded that our approach generates better quality test cases