Dynamic programming optimization algorithm applied in test case selection

In this paper we propose a quadratic dynamic programming algorithm applied in software testing domain, more specific in the test case selection decision making. We addressed a specific problem in software testing: running a subset of test cases from the whole set of available test cases in a limited time frame with the goal of maximizing the chances of finding potential defects. We employed both objective methods as the dynamic programming algorithm and subjective and empiric human decision as defining the selection and prioritization criteria. The proposed solution is suited for medium to large projects where in the worst-case scenarios the memory space complexity of the proposed algorithm does not exceed the order of GBytes. The proposed optimization algorithm is presented in pseudocode along with the dynamic programming recurrence formula and potential selection criteria as currently used in the industry.