Predicting Fault-proneness of Object-Oriented System Developed with Agile Process using Learned Bayesian Network

Lianfa Li, Hareton Leung

2013

Abstract

In the prediction of fault-proneness in object-oriented (OO) systems, it is essential to have a good prediction method and a set of informative predictive factors. Although logistic regression (LR) and naïve Bayes (NB) have been used successfully for prediction of fault-proneness, they have some shortcomings. In this paper, we proposed the Bayesian network (BN) with data mining techniques as a predictive model. Based on the Chidamber and Kemerer’s (C-K) metric suite and the cyclomatic complexity metrics, we examine the difference in the performance of LR, NB and BN models for the fault-proneness prediction at the class level in continual releases (five versions) of Rhino, an open-source implementation of JavaScript written in Java. From the viewpoint of modern software development, Rhino uses a highly iterative or agile development methodology. Our study demonstrates that the proposed BN can achieve a better prediction than LR and NB for the agile software.

Paper Citation

in Harvard Style

Li L. and Leung H. (2013). Predicting Fault-proneness of Object-Oriented System Developed with Agile Process using Learned Bayesian Network . In Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-989-8565-60-0, pages 5-16. DOI: 10.5220/0004392900050016

in Bibtex Style

@conference{iceis13,
author={Lianfa Li and Hareton Leung},
title={Predicting Fault-proneness of Object-Oriented System Developed with Agile Process using Learned Bayesian Network },
booktitle={Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 2: ICEIS,},
year={2013},
pages={5-16},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004392900050016},
isbn={978-989-8565-60-0},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 15th International Conference on Enterprise Information Systems - Volume 2: ICEIS,
TI - Predicting Fault-proneness of Object-Oriented System Developed with Agile Process using Learned Bayesian Network
SN - 978-989-8565-60-0
AU - Li L.
AU - Leung H.
PY - 2013
SP - 5
EP - 16
DO - 10.5220/0004392900050016