CGWO: An Improved Grey Wolf Optimization Technique for Test Case Prioritization

Abstract

The convergence rate has been widely accepted as a performance measure for choosing a better metaheuristic algorithm. So, we propose a novel technique to improve the performance of the existing Grey Wolf Optimization (GWO) algorithm in terms of its convergence rate. The proposed approach also prioritizes the test cases that are obtained after executing the input benchmark programs. This paper has three technical contributions. In our first contribution, we generate test cases for the input benchmark programs. Our second contribution prioritizes test cases using an improved version of the existing GWO algorithm (CGWO). Our third contribution analyzes the obtained result and compares it with state-of-the-art metaheuristic techniques. This work is validated after running the proposed model on six benchmark programs. The obtained results show that our proposed approach has achieved 48% better APFD score for the prioritized order of test cases than the non-prioritized order. We also achieved a better convergence rate, which takes around 4000 fewer iterations, when compared with the existing methods on the same platform.