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Mining software repositories for comprehensible software fault prediction models

Mining software repositories for comprehensible software fault prediction models
Mining software repositories for comprehensible software fault prediction models
Software managers are routinely confronted with software projects that contain errors or inconsistencies and exceed budget and time limits. By mining software repositories with comprehensible data mining techniques, predictive models can be induced that offer software managers the insights they need to tackle these quality and budgeting problems in an efficient way. This paper deals with the role that the Ant Colony Optimization (ACO)-based classification technique AntMiner+ can play as a comprehensible data mining technique to predict erroneous software modules. In an empirical comparison on three real-world public datasets, the rule-based models produced by AntMiner+ are shown to achieve a predictive accuracy that is competitive to that of the models induced by several other included classification techniques, such as C4.5, logistic regression and support vector machines. In addition, we will argue that the intuitiveness and comprehensibility of the AntMiner+ models can be considered superior to the latter models.
classification, software mining, fault prediction, comprehensibility, ant colony optimization
0164-1212
823-839
Vandecruys, Olivier
58fd2f26-ab16-4c20-92c7-7f66ea1b306b
Martens, David
42e7e141-fb3d-4ead-8e3a-96b39bab65f9
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934
De Backer, Manu
9c56870f-a34a-4eba-87ef-137fec532349
Haesen, Raf
82a78c40-85f2-4d67-9b26-b3e66b7808e3
Vandecruys, Olivier
58fd2f26-ab16-4c20-92c7-7f66ea1b306b
Martens, David
42e7e141-fb3d-4ead-8e3a-96b39bab65f9
Baesens, Bart
f7c6496b-aa7f-4026-8616-ca61d9e216f0
Mues, Christophe
07438e46-bad6-48ba-8f56-f945bc2ff934
De Backer, Manu
9c56870f-a34a-4eba-87ef-137fec532349
Haesen, Raf
82a78c40-85f2-4d67-9b26-b3e66b7808e3

Vandecruys, Olivier, Martens, David, Baesens, Bart, Mues, Christophe, De Backer, Manu and Haesen, Raf (2008) Mining software repositories for comprehensible software fault prediction models. [in special issue: Software Process and Product Measurement] Journal of Systems and Software, 81 (5), 823-839. (doi:10.1016/j.jss.2007.07.034).

Record type: Article

Abstract

Software managers are routinely confronted with software projects that contain errors or inconsistencies and exceed budget and time limits. By mining software repositories with comprehensible data mining techniques, predictive models can be induced that offer software managers the insights they need to tackle these quality and budgeting problems in an efficient way. This paper deals with the role that the Ant Colony Optimization (ACO)-based classification technique AntMiner+ can play as a comprehensible data mining technique to predict erroneous software modules. In an empirical comparison on three real-world public datasets, the rule-based models produced by AntMiner+ are shown to achieve a predictive accuracy that is competitive to that of the models induced by several other included classification techniques, such as C4.5, logistic regression and support vector machines. In addition, we will argue that the intuitiveness and comprehensibility of the AntMiner+ models can be considered superior to the latter models.

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More information

Published date: May 2008
Additional Information: In special issue: Software Process and Product Measurement
Keywords: classification, software mining, fault prediction, comprehensibility, ant colony optimization

Identifiers

Local EPrints ID: 80445
URI: http://eprints.soton.ac.uk/id/eprint/80445
ISSN: 0164-1212
PURE UUID: 19f84560-3bb7-43c3-bd6c-7a893112e1e7
ORCID for Bart Baesens: ORCID iD orcid.org/0000-0002-5831-5668
ORCID for Christophe Mues: ORCID iD orcid.org/0000-0002-6289-5490

Catalogue record

Date deposited: 24 Mar 2010
Last modified: 14 Mar 2024 02:49

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Contributors

Author: Olivier Vandecruys
Author: David Martens
Author: Bart Baesens ORCID iD
Author: Christophe Mues ORCID iD
Author: Manu De Backer
Author: Raf Haesen

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