Abstract 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.
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2012, Vol. 25 
