Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/12999
Title: Empirical analysis of object-oriented design metrics for predicting high and low severity faults
Authors: Zhou, Y
Leung, H 
Keywords: Cross validation
Fault-proneness
Faults
Metrics
Object-oriented
Prediction
Issue Date: 2006
Publisher: Institute of Electrical and Electronics Engineers
Source: IEEE transactions on software engineering, 2006, v. 32, no. 10, p. 771-789 How to cite?
Journal: IEEE transactions on software engineering 
Abstract: In the last decade, empirical studies on object-oriented design metrics have shown some of them to be useful for predicting the fault-proneness of classes in object-oriented software systems. This research did not, however, distinguish among faults according to the severity of impact. It would be valuable to know how object-oriented design metrics and class fault-proneness are related when fault severity is taken into account. In this paper, we use logistic regression and machine learning methods to empirically investigate the usefulness of object-oriented design metrics, specifically, a subset of the Chidamber and Kemerer suite, in predicting fault-proneness when taking fault severity into account. Our results, based on a public domain NASA data set, indicate that 1) most of these design metrics are statistically related to fault-proneness of classes across fault severity, and 2) the prediction capabilities of the investigated metrics greatly depend on the severity of faults. More specifically, these design metrics are able to predict low severity faults in fault-prone classes better than high severity faults in fault-prone classes.
URI: http://hdl.handle.net/10397/12999
ISSN: 0098-5589
EISSN: 1939-3520
DOI: 10.1109/TSE.2006.102
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