General Optimization Strategies for Refining the In-Parameter-Order Algorithm

Abstract

In-Parameter-Order (IPO) algorithm is an effective strategy of combinatorial testing. And several variants of the algorithm have been developed for reducing the runtime and size of test cases or for dealing with certain problems in test case generation, such as IPOG, IPOG-F and IPOG-F2. In this paper, the general optimization strategies, which can be applied to these variants of the algorithm, are proposed to make each value of all parameters more evenly distributed in the test cases. The proposed optimization strategies mainly focus on choosing values for the extension to an additional parameter during the horizontal growth of the algorithm and filling values for don't care positions. Experimental results show that the proposed optimization strategies are effective in reducing runtime and producing smaller size of test suites with the increase of the domain size.