Selection of human evaluators for design smell detection using dragonfly optimization algorithm: An empirical study

• Published in: Information and software technology 2023-03, Vol.155, p.107120, Article 107120
• Main Authors: Al Khatib, Sultan M., Alkharabsheh, Khalid, Alawadi, Sadi
• Format: Article
• Language: English
• Subjects: Design smell detection; Dragonfly Algorithm; Empirical study; God class; Optimization; Search-based software engineering; Software quality
• ISSN: 0950-5849; 1873-6025; 1873-6025
• DOI: 10.1016/j.infsof.2022.107120

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.