TEST CASE PRIORITIZATION USING MACHINE LEARNING TECHNIQUES

Abstract

As the complexity of software is increasing, generating an effective test data has become a necessity. This necessity has increased the demand for techniques that can generate test data effectively. In this research work we propose prioritized test cases generation techniques on the basis of machine learning algorithm. We have devised two test case prioritization technique using Genetic algorithm and particle swarm optimization algorithm. Then we use the concept of mutation analysis to check the adequacy of the prioritized sequence. This is the sequence of test cases according to their ability to detect error. That means the test cases which is more likely to find the more errors should be executed first. This technique saves significant amount of time in regression testing.