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Study on Residual Defect Prediction using Multiple Technologies

Wanjiang. Han1, Lixin. Jiang2, Tianbo. Lu3, Xiaoyan. Zhang,3, and Sun Yi3
1. School Of Software Engineering, Beijing University of Posts and Telecommunication, Beijing, ChinaRiyadh, Saudi Arabia
2. Department of Emergency Response, China Earthquake Networks Center, Beijing, China
3. School Of Software Engineering, Beijing University of Posts and Telecommunication, Beijing, China

Abstract— Finding defects in a software system is not easy. Effective detection of software defects is an important activity of software development process. In this paper, we propose an approach to predict residual defects, which applies machine learning algorithms (classifiers) and defect distribution model. This approach includes two steps. Firstly, use machine learning Algorithms to get defect classification table, then confirm the defect distribution trend referring to several distribution models. Experiment results on a GUI project show that the approach can effectively improve the accuracy of defect prediction and be used for test planning and implementation.

Index Terms— residual defect prediction, defect distribution model, software defect classification, defect trend, classifiers

Cite: Wanjiang. Han, Lixin. Jiang, Tianbo. Lu,Xiaoyan. Zhang, Sun Yi " Study on Residual Defect Prediction using Multiple Technologies, Vol. 5, No. 3, pp. 79-85, August, 2014.doi: doi:10.4304/jait.5.3.79-85

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