Abductive Network Ensembles for Improved
Prediction of Future Change-Prone Classes
in Object-Oriented Software
Mojeeb
Al-Khiaty1, Radwan Abdel-Aal2, and Mahmoud Elish1,3
1Information and Computer Science
Department, King Fahd University of Petroleum and Minerals, Saudi Arabia
2Computer Engineering Department,
King Fahd University of Petroleum and Minerals, Saudi Arabia
3Computer Science Department, Gulf
University for Science and Technology, Kuwait
Abstract: Software
systems are subject to a series of changes due to a variety of maintenance
goals. Some parts of the software system are more prone to changes than others.
These change-prone parts need to be identified so that maintenance resources
can be allocated effectively. This paper proposes the use of Group Method of
Data Handling (GMDH)-based abductive networks for modeling and predicting
change proneness of classes in object-oriented software using both software
structural properties (quantified by the C&K metrics) and software change
history (quantified by a set of evolution-based metrics) as predictors. The
empirical results derived from an experiment conducted on a case study of an
open-source system show that the proposed approach improves the prediction
accuracy as compared to statistical-based prediction models.
Keywords: Change-proneness,
software metrics, abductive networks, ensemble classifiers.
Received June 2, 2015; accepted September 20,
2015