Abductive Network Ensembles for Improved

Prediction of Future Change-Prone Classes

in Object-Oriented Software

Mojeeb Al-Khiaty¹, Radwan Abdel-Aal², and Mahmoud Elish^1,3

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