  It is common practice to discretize continuous defect counts into defective
and non-defective classes and use them as a target variable when building
defect classifiers (discretized classifiers). However, this discretization of
continuous defect counts leads to information loss that might affect the
performance and interpretation of defect classifiers. Another possible approach
to build defect classifiers is through the use of regression models then
discretizing the predicted defect counts into defective and non-defective
classes (regression-based classifiers).
  In this paper, we compare the performance and interpretation of defect
classifiers that are built using both approaches (i.e., discretized classifiers
and regression-based classifiers) across six commonly used machine learning
classifiers (i.e., linear/logistic regression, random forest, KNN, SVM, CART,
and neural networks) and 17 datasets. We find that: i) Random forest based
classifiers outperform other classifiers (best AUC) for both classifier
building approaches; ii) In contrast to common practice, building a defect
classifier using discretized defect counts (i.e., discretized classifiers) does
not always lead to better performance.
  Hence we suggest that future defect classification studies should consider
building regression-based classifiers (in particular when the defective ratio
of the modeled dataset is low). Moreover, we suggest that both approaches for
building defect classifiers should be explored, so the best-performing
classifier can be used when determining the most influential features.
