Diversity Based Genetic Algorithm for Efficient Test Case Selection

Abstract: The testing is a very important innovate the event of the software system cycle. The test cases are generated multiple test suite. The regression testing is done to reduce the effort of testing by selecting a subset of test cases from the test suite with respect to some testing criteria. Combination of Greedy and multi-objective genetic algorithms (MOGAs) does not produce better results. The greedy algorithm which is used to find optimal solution reduces the time but increases cost. Genetic also find next generation of test case selection from the test suite. Hence by injecting the new diversity based genetic algorithm (DIV-GA) during the search process a better solution is provided for the detection of test cases. A way to reduce the cost of regression testing consists of selection or prioritizing subset of test cases. Therefore by selecting and prioritizing the subset of test cases and given as input to DIV-GA reduce the time & cost estimation of efficient test cases.

Keywords: Regression Testing; Greedy Algorithm; Multi-Objective genetic algorithm (MOGA); Diversity Based Genetic Algorithm (DIV-GA); Test case selection.