An Insight to Software Testing using Genetic Algorithm

Many techniques have been discovered by the researcher’s based on the minimization of test suites. Software testing ingests about 50% of the total time, cost and resources and this time is even higher if the system is safety critical. This is because the code scope of Software under Test (SUT) is very big. Numerous test cases can be produced for a small piece of code and the testing of all these test suites is not feasible due to the availability of limited time and resources. So, to save the time and resources these test cases should be minimized by choosing the group of test suites that has the greatest likelihood of uncovering the bugs. This paper is focused on minimization of test cases using Genetic Algorithms. This paper also presents the comparison of various testing techniques. The results of two techniques GA with sigma scaling and GA with class partitioning are compared. On the basis of fitness value both the techniques are evaluated. Minimum and maximum fitness values are 44 and 204 using technique 1 (GA with sigma scaling) while 76 and 236 are minimum and maximum fitness values using technique 2 (GA with class partitioning). The goal of this study is to provide the direction to software developer about various testing techniques.