A novel kernel learning method for object-oriented （00） 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 a...A novel kernel learning method for object-oriented （00） 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 vali- dated using both an artificial dataset and a case of industrial software from the optical communication field. Preliminary experi- ments 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.展开更多
This paper presents software reliability growth models(SRGMs) with change-point based on the stochastic differential equation(SDE).Although SRGMs based on SDE have been developed in a large scale software system,consi...This paper presents software reliability growth models(SRGMs) with change-point based on the stochastic differential equation(SDE).Although SRGMs based on SDE have been developed in a large scale software system,considering the variation of failure distribution in the existing models during testing time is limited.These SDE SRGMs assume that failures have the same distribution.However,in practice,the fault detection rate can be affected by some factors and may be changed at certain point as time proceeds.With respect to this issue,in this paper,SDE SRGMs with changepoint are proposed to precisely reflect the variations of the failure distribution.A real data set is used to evaluate the new models.The experimental results show that the proposed models have a fairly accurate prediction capability.展开更多
文摘A novel kernel learning method for object-oriented （00） 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 vali- dated using both an artificial dataset and a case of industrial software from the optical communication field. Preliminary experi- ments 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.
基金Supported by the International Science&Technology Cooperation Program of China(No.2010DFA14400)the National Natural Science Foundation of China(No.60503015)the National High Technology Research and Development Programme of China(No.2008AA01A201)
文摘This paper presents software reliability growth models(SRGMs) with change-point based on the stochastic differential equation(SDE).Although SRGMs based on SDE have been developed in a large scale software system,considering the variation of failure distribution in the existing models during testing time is limited.These SDE SRGMs assume that failures have the same distribution.However,in practice,the fault detection rate can be affected by some factors and may be changed at certain point as time proceeds.With respect to this issue,in this paper,SDE SRGMs with changepoint are proposed to precisely reflect the variations of the failure distribution.A real data set is used to evaluate the new models.The experimental results show that the proposed models have a fairly accurate prediction capability.
