Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16465
Title: Bayesian prediction of fault-proneness of agile-developed object-oriented system
Authors: Li, L
Leung, H 
Keywords: Agile process
Data mining
Fault-proneness
Object-oriented systems
Software quality
Issue Date: 2014
Publisher: Springer Verlag
Source: Lecture notes in business information processing, 2014, v. 190, p. 209-225 How to cite?
Journal: Lecture Notes in Business Information Processing 
Abstract: Logistic regression (LR) and naïve Bayes (NB) extensively used for prediction of fault-proneness assume linear addition and independence that often cannot hold in practice. Hence, we propose a Bayesian network (BN) model with incorporation of data mining techniques as an integrative approach. Compared with LR and NB, BN provides a flexible modeling framework, thus avoiding the corresponding assumptions. Using the static metrics such as Chidamber and Kemerer’s (C-K) suite and complexity as predictors, the differences in performance between LR, NB and BN models were examined for fault-proneness prediction at the class level in continual releases (five versions) of Rhino, an open-source implementation of JavaScript, developed using the agile process. By cross validation and independent test of continual versions, we conclude that the proposed BN can achieve a better prediction than LR and NB for the agile software due to its flexible modeling framework and incorporation of multiple sophisticated learning algorithms.
Description: 15th International Conference on Enterprise Information Systems, ICEIS 2013, France, 4-7 July 2013
URI: http://hdl.handle.net/10397/16465
ISBN: 9783319094915
ISSN: 1865-1348
DOI: 10.1007/978-3-319-09492-2_13
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