A Hybrid Test Optimization Framework - Coupling Genetic Algorithm with Local Search Technique

Abstract

Quality of test cases is determined by their ability to uncover as many errors 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 have higher mutation score and path coverage from a near infinite number of test cases. Hence, the final test set size is reduced which in turn reduces the total time 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 test cases generated by our approach against the existing test case optimization approaches such as Simple Genetic Algorithm (SGA) and Bacteriologic Algorithm (BA) and concluded that our approach generates better quality test cases.