A Path-Oriented Test Data Generation Approach Hybridizing Genetic Algorithm and Artificial Immune System

Validating the correctness of software through a tool has started gaining a wide foothold in the business. A test data generator is one such tool which automatically generates the test data for software so as to attain maximum coverage. Researchers in the past have adopted different evolutionary algorithms to automatically generate a data set. One such often used procedure is Genetic Algorithm (GA). Due to certain flaws present in this approach, we have redefined the cause of concern for coverage in structural testing. In this paper, we have explored the properties of immune system along with GA. We have proposed a new hybrid algorithm-GeMune algorithm-inspired from these biological backdrops. Experimental results certify that the new algorithm has a better coverage compared to the use of only Genetic Algorithm for structural testing.