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.

Design smell detection has proven to be a significant activity that has an aim of not only enhancing the software quality but also increasing its life cycle. This work investigates whether machine learning approaches can effectively be leveraged for software design smell detection. Additionally, this paper provides a comparatively study, focused on using balanced datasets, where it checks if avoiding dataset balancing can be of any influence on the accuracy and behavior during design smell detection. A set of experiments have been conducted-using 28 Machine Learning classifiers aimed at detecting God classes. This experiment was conducted using a dataset formed from 12,587 classes of 24 software systems, in which 1,958 classes were manually validated. Ultimately, most classifiers obtained high performances,-with Cat Boost showing a higher performance. Also, it is evident from the experiments conducted that data balancing does not have any significant influence on the accuracy of detection. This reinforces the application of machine learning in real scenarios where the data is usually imbalanced by the inherent nature of design smells. Machine learning approaches can effectively be used as a leverage for God class detection. While in this paper we have employed SMOTE technique for data balancing, it is worth noting that there exist other methods of data balancing and with other design smells. Furthermore, it is also important to note that application of those other methods may improve the results, in our experiments SMOTE did not improve God class detection. The results are not fully generalizable because only one design smell is studied with projects developed in a single programming language, and only one balancing technique is used to compare with the imbalanced case. But these results are promising for the application in real design smells detection scenarios as mentioned above and the focus on other measures, such as Kappa, ROC, and MCC, have been used in the assessment of the classifier behavior.

Design Smell detection has proven to be an effective strategy to improve software quality and consequently decrease maintainability expenses.In this work, we explore the influence of the size category of the software project on the automatic detection of God Class Design Smell by different machine learning techniques.A set of experiments were conducted with eight different learning classifiers on a dataset formed by 12,588 classes of 24 systems.The results were evaluated using ROC area and Kappa tests.The classifiers change their behaviour when they are used in sets that differ in the value of the selected size information of their classes.This study concludes that it is possible to improve results, mainly in agreement, of God Class detection feeding machine learning classifiers with project size information of the classes to analyze.