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.

设计气味检测已被证明是提高软件质量并因此减少可维护性费用的有效策略。这项工作探索了表示为项目领域和大小类别信息的项目上下文信息对通过机器学习技术自动检测神级设计气味的影响。在包含24个Java项目中的12个，587个类的数据集上进行了使用八个分类器检测神类的一组实验。结果表明，分类器用于这些类型的项目信息不同的数据集时会改变其行为。结果表明神课 通过向机器学习分类器提供此项目上下文信息，可以改善设计气味检测。