Selection of human evaluators for design smell detection using dragonfly optimization algorithm: An empirical study
Sultan M. Al Khatib, Khalid Alkharabsheh, Sadi A. Alawadi · Information and Software Technology · 2022
Design smell detection is considered an efficient activity that decreases maintainability expenses and improves software quality. Human context plays an essential role in this domain. In this paper, we propose a search-based approach to optimize the selection of human evaluators for design smell detection. For this purpose, Dragonfly Algorithm (DA) is employed to identify the optimal or near-optimal human evaluator’s profiles. An online survey is designed and asks the evaluators to evaluate a sample of classes for the presence of god class design smell. The Kappa-Fleiss test has been used to validate the proposed approach. The results show that the dragonfly optimization algorithm can be utilized effectively to decrease the efforts (time, cost ) of design smell detection concerning the identification of the number and the optimal or near-optimal profile of human experts required for the evaluation process. A Search-based approach can be effectively used for improving a god-class design smell detection. Consequently, this leads to minimizing the maintenance cost.