A Genetic Algorithm for Regression Test Sequence Optimization

Abstract: Regression testing is the process of validating modified software to assure that changed parts of software behave as intended and unchanged parts of software have not been adversely affected by the modification. The regression test suite is typically large and needs an intelligent method to choose those test cases which will reduce the overall test cost. In this situation, test case prioritization techniques aim to improve the effectiveness of regression testing by ordering the test cases so that the most beneficial are executed first. In this approach, a new Genetic Algorithm to prioritize the regression test suite is introduced that will prioritize test cases dynamically on the basis of complete code coverage. Meanwhile, an approach to generating new test cases is presented using PMX and cyclic crossover and analysis is done on the basis of process cost and test cost. The overall aim of this research is to reduce the number of test cases that need to be run after changes have been made.

Keywords: Regression Testing, Dynamic Prioritization, Fitness Function, Mutation, Cross Over.