DocumentCode :
803118
Title :
Empirical Analysis of Object-Oriented Design Metrics for Predicting High and Low Severity Faults
Author :
Zhou, Yuming ; Leung, Hareton
Author_Institution :
Dept. of Comput., Hong Kong Polytech. Univ., Kowloon
Volume :
32
Issue :
10
fYear :
2006
Firstpage :
771
Lastpage :
789
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
Keywords :
object-oriented programming; regression analysis; software fault tolerance; software metrics; fault severity; fault-prone classes; fault-proneness prediction; logistic regression method; machine learning method; object-oriented design metrics; object-oriented software system; public domain NASA data set; Computer Society; Decision making; Fault detection; Learning systems; Logistics; NASA; Object oriented modeling; Predictive models; Programming; Software systems; Object-oriented; cross validation.; fault-proneness; faults; metrics; prediction;
fLanguage :
English
Journal_Title :
Software Engineering, IEEE Transactions on
Publisher :
ieee
ISSN :
0098-5589
Type :
jour
DOI :
10.1109/TSE.2006.102
Filename :
1717471
Link To Document :
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