Test case prioritization techniques “an empirical study”

Abstract

Regression testing is an expensive process. A number of methodologies of regression testing are used to improve its effectiveness. These are retest all, test case selection, test case reduction and test case prioritization. Retest all technique involves re-execution of all available test suites, which are critical moreover cost effective. In order to increase efficiency, test case prioritization is being utilized for rearranging the test cases. A number of algorithms has been stated in the literature survey such as Greedy Algorithms and Metaheuristic search algorithms. A simple greedy algorithm focuses on test case prioritization but results in less efficient manner, due to which researches moved towards the additional greedy and 2-Optimal algorithms. Forthcoming metaheuristic search technique (Hill climbing and Genetic Algorithm) produces a much better solution to the test case prioritization problem. It implements stochastic optimization while dealing with problem concern. The genetic algorithm is an evolutionary algorithm which gives an exact mathematical fitness value for the test cases on which prioritization is done. This paper focuses on the comparison of metaheuristic genetic algorithm with other algorithms and proves the efficiency of genetic algorithm over the remaining ones.