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ZJUS-A
Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering)  2008, Vol. 9 Issue (10): 1390-1397    DOI: 10.1631/jzus.A0720073
Electrical & Electronic Engineering     
Predicting the fault-proneness of class hierarchy in object-oriented software using a layered kernel
Peng HUANG, Jie ZHU
Department of Electronic Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Abstract  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.

Key words： Object-oriented software      Fault-proneness      Support vector machine      Structured kernel     
Received: 22 November 2007     
CLC:  TN914  
  TP311  
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Peng HUANG
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Cite this article:

Peng HUANG, Jie ZHU. Predicting the fault-proneness of class hierarchy in object-oriented software using a layered kernel. Journal of Zhejiang University-SCIENCE A (Applied Physics & Engineering), 2008, 9(10): 1390-1397.

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http://www.zjujournals.com/xueshu/zjus-a/10.1631/jzus.A0720073     OR     http://www.zjujournals.com/xueshu/zjus-a/Y2008/V9/I10/1390

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