Design smell detection has proven to be an efficient strategy to improve software quality and consequently decrease maintainability expenses. This work explores the influence of the information about project context expressed as project domain and size category information, on the automatic detection of the god class design smell by machine learning techniques. A set of experiments using eight classifiers to detect god classes was conducted on a dataset containing 12, 587 classes from 24 Java projects. The results show that classifiers change their behavior when they are used on datasets that differ in these kinds of project information. The results show that god class design smell detection can be improved by feeding machine learning classifiers with this project context information.
Keywords: Design smell detection, Machine learning, Software metrics, Project context information, God class