| Abbreviations | Description |
| PCA | Principal component analysis (PCA) is a standard statistical procedure to convert a set of possibly correlated variables into a (typically smaller) set of linearly uncorrelated variables by using a coordinate transformation. |
| R2 | R squared: coefficient of determination, measures the variance in the predicted variable that is accounted by the regression built using the predictors (code metrics combined with static analysis fault density). |
| MSE | Mean squared error (MSE) is a measure of the unbiased error estimate of the error variance. |
| ROC curve | Receiver operating characteristic (ROC) curve, is a popular measure for evaluating classifier performance. The ROC curve is created by plotting the true positive rate against the false positive rate at various threshold settings. |
| AUC | Area under curve (AUC) equals the probability that the classifier predicts a randomly chosen true positive higher than a randomly chosen false negative. The larger the AUC, the more accurate is the classification model. |