RT info:eu-repo/semantics/article
T1 Exploratory study of the impact of project domain and size category on the detection of the God class design smell
A1 Alkharabsheh, Khalid
A1 Crespo, Yania
A1 Fernández-Delgado, Manuel
A1 Viqueira, José R.
A1 Taboada, José A.
AB 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.
PB Springer Nature
SN 0963-9314
YR 2021
FD 2021
LK https://uvadoc.uva.es/handle/10324/74394
UL https://uvadoc.uva.es/handle/10324/74394
LA eng
NO Software Quality Journal 29, 197–237, 2021
DS UVaDOC
RD 05-feb-2025