A novel privacy preserving based ensemble cross defect prediction model for decision making

Nageswara Rao Moparthi, N. Geethanjali
2016 Perspectives in Science  
In recent years, defect prediction and severity assessment have been successfully applied in software defects and metrics prediction in business applications. Providing essential security to the software metrics and decision patterns are the two main issues in the traditional business models for inter and intra communication mechanisms. Traditional software prediction and decision pattern models are important to the business analysts for decision making and market analysis. But these models
more » ... d not provide enough privacy to the business models for secure transmission of the decision patterns. In this proposed model, a new privacy preserving based defect prediction classification model was implemented on multiple associated products to predict metric relationship, along with defects patterns. Experimental results show that proposed model has high true positive rate compared to traditional Bayesian network and privacy preserving models. Also, this model generates a high privacy preserved decision patterns on various business software applications for secure communication.
doi:10.1016/j.pisc.2016.03.014 fatcat:4i3rgb42w5gptpbntdmknfdq5m