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.

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[37] Thwin M. and Quah T., Application of Neural Networks for Estimating Software Maintainability Using Object-Oriented Metrics, in Proceeding of International Conference on Software Engineering and Knowledge Engineering, San Francisco, pp. 69-73, 2003. Mojeeb AL-Khiaty received his BS degree in Mathematics and Computer from Sana a University, Yemen, in 1999, his MS degree in Computer Science from King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia, in 2009, and his PhD degree in Computer Science and Engineering from KFUPM, Saudi Arabia, in 2015. His research interests include software engineering, software metrics, software reuse, and soft computing. Radwan Abdel-Aal received his BS in electrical engineering from Cairo University, Egypt, in 1972, his MS in aviation electronics from Cranfield University, UK in 1974, and his PhD from Strathclyde University, UK in 1983. Between 1985 and 2005 he was a research scientist at the Research Institute of King Fahd University of Petroleum and Minerals (KFUPM), Dhahran, Saudi Arabia. In 2005 he joined the Computer Engineering Department at KFUPM where he is currently a Professor. His research interests include nuclear physics instrumentation and machine learning and data mining applications. Mahmoud Elish is an Associate Professor in the Computer Science Department at Gulf University for Science and Technology (GUST), Kuwait. He has been an Associate Professor in the Information and Computer Science Department at King Fahd University of Petroleum and Minerals (KFUPM), Saudi Arabia. He received his PhD from George Mason University. His research interests include software metrics, design, quality and maintenance.