Title
Mining software repositories for comprehensible software fault prediction modelsMining software repositories for comprehensible software fault prediction models
Author
Faculty/Department
Faculty of Applied Economics
Research group
Management Information Systems (MIS)
Engineering Management
Publication type
article
Publication
New York,
Subject
Computer. Automation
Source (journal)
Journal of systems and software. - New York
Volume/pages
81(2008):5, p. 823-839
ISSN
0164-1212
ISI
000255295900016
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
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.
E-info
https://repository.uantwerpen.be/docman/iruaauth/eebdcf/031e612eee0.pdf
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