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Towards comprehensible software fault prediction models using Bayesian network classifiers

Towards comprehensible software fault prediction models using Bayesian network classifiers
Towards comprehensible software fault prediction models using Bayesian network classifiers
Software testing is a crucial activity during software development and fault prediction models assist practitioners herein by providing an upfront identification of faulty software code by drawing upon the machine learning literature. While especially the Naive Bayes classifier is often applied in this regard, citing predictive performance and comprehensibility as its major strengths, a number of alternative Bayesian algorithms that boost the possibility to construct simpler networks with less nodes and arcs remain unexplored. This study contributes to the literature by considering 15 different Bayesian Network (BN) classifiers and comparing them to other popular machine learning techniques. Furthermore, the applicability of the Markov blanket principle for feature selection, which is a natural extension to BN theory, is investigated. The results, both in terms of the AUC and the recently introduced H-measure, are rigorously tested using the statistical framework of Demsar. It is concluded that simple and comprehensible networks with less nodes can be constructed using BN classifiers other than the Naive Bayes classifier. Furthermore, it is found that the aspects of comprehensibility and predictive performance need to be balanced out, and also the development context is an item which should be taken into account during model selection.
Dejaeger, Karel
491728b5-118c-4661-b003-3787037c8f2d
Verbraken, Thomas
40def165-29ac-4a4d-8820-f434ea123b96
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Dejaeger, Karel
491728b5-118c-4661-b003-3787037c8f2d
Verbraken, Thomas
40def165-29ac-4a4d-8820-f434ea123b96
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0

Dejaeger, Karel, Verbraken, Thomas and Baesens, Bart (2012) Towards comprehensible software fault prediction models using Bayesian network classifiers. IEEE Transactions on Software Engineering. (doi:10.1109/TSE.2012.20). (In Press)

Record type: Article

Abstract

Software testing is a crucial activity during software development and fault prediction models assist practitioners herein by providing an upfront identification of faulty software code by drawing upon the machine learning literature. While especially the Naive Bayes classifier is often applied in this regard, citing predictive performance and comprehensibility as its major strengths, a number of alternative Bayesian algorithms that boost the possibility to construct simpler networks with less nodes and arcs remain unexplored. This study contributes to the literature by considering 15 different Bayesian Network (BN) classifiers and comparing them to other popular machine learning techniques. Furthermore, the applicability of the Markov blanket principle for feature selection, which is a natural extension to BN theory, is investigated. The results, both in terms of the AUC and the recently introduced H-measure, are rigorously tested using the statistical framework of Demsar. It is concluded that simple and comprehensible networks with less nodes can be constructed using BN classifiers other than the Naive Bayes classifier. Furthermore, it is found that the aspects of comprehensibility and predictive performance need to be balanced out, and also the development context is an item which should be taken into account during model selection.

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More information

Accepted/In Press date: March 2012
Organisations: Southampton Business School

Identifiers

Local EPrints ID: 336465
URI: http://eprints.soton.ac.uk/id/eprint/336465
PURE UUID: 4a641fcb-cb10-4ba8-a0de-cb850db4e4a6
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668

Catalogue record

Date deposited: 27 Mar 2012 10:44
Last modified: 17 Dec 2019 01:47

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Contributors

Author: Karel Dejaeger
Author: Thomas Verbraken
Author: Bart Baesens ORCID iD

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