Improving GA based automated test data generation technique for object oriented software

Genetic algorithms have been successfully applied in the area of software testing. The demand for automation of test case generation in object oriented software testing is increasing. Extensive tests can only be achieved through a test automation process. The benefits achieved through test automation include lowering the cost of tests and consequently, the cost of whole process of software development. Several studies have been performed using this technique for automation in generating test data but this technique is expensive and cannot be applied properly to programs having complex structures. Since, previous approaches in the area of object-oriented testing are limited in terms of test case feasibility due to call dependences and runtime exceptions. This paper proposes a strategy for evaluating the fitness of both feasible and unfeasible test cases leading to the improvement of evolutionary search by achieving higher coverage and evolving more number of unfeasible test cases into feasible ones.