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Defect Number Prediction (DNP) models can offer more benefits than classification-based defect prediction. Recently, many researchers proposed to employ regression algorithms for DNP, and found that the algorithms achieve low Average Absolute Error (AAE) and high Pred(0.3) values. However, since the defect datasets generally contain many non-defective modules, even if a DNP model predicts the number of defects in all modules as zero, the AAE value of the model will be low and Pred(0.3) value will be high. Therefore, the good performance of the regression algorithms in terms of AAE and Pred(0.3) may be questioned due to the imbalanced distribution of the number of defects.

To revisit the impact of regression algorithms for predicting the precise number of defects.

We examine the practical effects of 12 widely-used regression algorithms, two data resampling algorithm (SmoteR …