Assessing the Influence of Size Category of the Project in God Class Detection, an Experimental Approach based on Machine Learning (MLA)
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.
keywords: Design Smell Detection, Machine Learning, God Class