Model based test case generation and optimization using intelligent optimization agent

Abstract

Test case optimization is one of the techniques which efficiently manage the exponential growth in time and cost of testing. But in many times the researchers compromise with the code coverage while going for optimization. In this paper, the test suite is optimized using Intelligent Optimization Agent (IOA) while the keeping the percentage of code coverage unchanged. First the System Under Test (SUT) is modelled using UML Activity Diagram (AD) and converted into an Activity Graph (AG). Then the optimized path is found out in AD by using IOA and cost attributes. Then suitable algorithms are proposed to remove the redundant nodes in the optimized path. IOA is an agent based approach as compared to Hybrid Genetic Algorithm (HGA) in Intelligent Test Optimization Agent (ITOA).The proposed approach is found to be effective when compared with other optimization techniques like Genetic Algorithm (GA) and Intelligent Test Optimization Agent (ITOA).