Boosting-based k-NN learning for software defect prediction
Boosting-based k-NN learning for software defect prediction
复制标题
基于Boosting的k-NN学习用于软件缺陷预测
DOI：
--
复制
发表时间：
2012
期刊：
影响因子：
--
通讯作者：
Shen, Jun-Yi
中科院分区：
文献类型：
--
作者：
He, Liang;Song, Qin-Bao;Shen, Jun-Yi
Timely identification of defective modules improves both software quality and testing efficiency. A software metrics-based ensemble k-NN algorithm is proposed for software defect prediction. Firstly, a set of base k-NN predictors is constructed iteratively from different bootstrap sampling datasets. Next, the base k-NN predictors estimate the software module independently and their individual outputs are combined as the composite result. Then, an adaptive threshold training approach is designed for the ensemble to classify new software modules. If the composite result is greater than the threshold value, the software module is recognized as defective, otherwise as normal. Finally, the experiments are conducted on NASA MDP and PROMISE AR datasets. Compared with a widely referenced defect prediction approach, the results show the considerable improvements of the ensemble k-NN and prove the effectiveness of software metrics in defect prediction.
