MULTI-OBJECTIVE GENETIC ALGORITHM FOR REGRESSION TESTING REDUCTION

Abstract

Regression Testing is type of testing which is used to cut off the directly price associated with testing of different modules. Generally regression testing performs on test cases so that the resource utilization should be very lo w. Test cases in it are challenging task to achieve and suitable test cases for regression testing are difficult to judge and process. Optimization of various testing processes is done by optimization a lgorithms such as Genetic and Ant colony which usua lly provide solution to the good regression testing. Still these algorithms lac k of some features which are required for better op timization of test cases in regression testing. The regression testing is the most expensive phase of the software testing, regression testing reducti on eliminates the redundant test cases in the regressi on testing suite and saves the cost of the regressi on testing. In our proposed work, we will focus on optimization of regression t esting with multi-objective genetic algorithm which will cover parameters like simplicity and complexity for test cases for regres sion testing. The complexity and simplicity for tes t cases will be judged and according to a common fitness function threshold we will proceed with optimization of the regression tphases. Finally the paper evaluates the basic genetic algorithm for opt imizing the test cases based on execution time; imp lement the multi-objective genetic algorithm with simplicity and complexity of the test cases along with execution time for test case prioritization for regression testing.