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CLC number: TN914; TP311

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Received: 2007-11-22

Revision Accepted: 2008-06-26

Crosschecked: 0000-00-00

Cited: 3

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Citations:  Bibtex RefMan EndNote GB/T7714

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Journal of Zhejiang University SCIENCE A 2008 Vol.9 No.10 P.1390-1397


Predicting the fault-proneness of class hierarchy in object-oriented software using a layered kernel

Author(s):  Peng HUANG, Jie ZHU

Affiliation(s):  Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China

Corresponding email(s):   superhp@sjtu.edu.cn, zhujie@sjtu.edu.cn

Key Words:  Object-oriented software, Fault-proneness, Support vector machine, Structured kernel

Peng HUANG, Jie ZHU. Predicting the fault-proneness of class hierarchy in object-oriented software using a layered kernel[J]. Journal of Zhejiang University Science A, 2008, 9(10): 1390-1397.

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A1 - Jie ZHU
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0720073

A novel kernel learning method for object-oriented (OO) software fault prediction is proposed in this paper. With this method, each set of classes that has inheritance relation named class hierarchy, is treated as an elemental software model. A layered kernel is introduced to handle the tree data structure corresponding to the class hierarchy models. This method was validated using both an artificial dataset and a case of industrial software from the optical communication field. Preliminary experiments showed that our approach is very effective in learning structured data and outperforms the traditional support vector learning methods in accurately and correctly predicting the fault-prone class hierarchy model in real-life OO software.

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