  In current research, there are contrasting results about the applicability of
software source code metrics as features for defect prediction models. The goal
of the paper is to evaluate the adoption of software metrics in models for
software defect prediction, identifying the impact of individual source code
metrics. With an empirical study on 275 release versions of 39 Java projects
mined from GitHub, we compute 12 software metrics and collect software defect
information. We train and compare three defect classification models. The
results across all projects indicate that Decision Tree (DT) and Random Forest
(RF) classifiers show the best results. Among the highest-performing individual
metrics are NOC, NPA, DIT, and LCOM5. While other metrics, such as CBO, do not
bring significant improvements to the models.
