DDF: Diversity Dragonfly Algorithm for cost-aware test suite minimization approach for software testing

摘要

The test suite minimization approach is a major research topic that it requires huge attention from the researchers as the traditional methods used for performing the test suite minimization is concentrated on the cost of regression testing but the requirements were not satisfied. To solve the problem of satisfying requirements, researchers proposed greedy algorithms, optimization algorithms, and so on. In this paper, a novel optimization algorithm is proposed termed as the Diversity Dragonfly Algorithm (DDF) algorithm that concentrates on the cost and quality of the test suite. The diversification included in the standard Dragonfly algorithm forms the DDF that uses three bitwise operators for diversification. The DDF algorithm determines the best suite based on the hunting mechanism of the dragonfly using a minimum objective function such that the selected test suite satisfies all the requirements. The experiment is carried out using five subject programs and the performance analysis of the proposed DDF is carried out and compared with the existing methods. It is found that the reduction capability of the DDF is better than existing methods and the cost of the proposed DDF is low ensuring a quality test suite reduction.