Abductive network ensembles for improved prediction of future change-prone classes in object-oriented software

Mojeeb Al-Khiaty, Radwan Abdel-Aal, Mahmoud Elish

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

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.

Original languageEnglish
Pages (from-to)803-811
Number of pages9
JournalInternational Arab Journal of Information Technology
Volume14
Issue number6
StatePublished - Nov 2017

Bibliographical note

Publisher Copyright:
© 2017, Zarka Private University. All rights reserved.

Keywords

  • Abductive networks
  • Change-proneness
  • Ensemble classifiers
  • Software metrics

ASJC Scopus subject areas

  • General Computer Science

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