Source Code Metrics for Software Defects Prediction
| Authors | |
|---|---|
| Year of publication | 2023 |
| Type | Article in Proceedings |
| Conference | The 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23) |
| MU Faculty or unit | |
| Citation | |
| Doi | http://dx.doi.org/10.1145/3555776.3577809 |
| Keywords | Software Defect ; Software Metrics; Mining Software Repositories; Software Quality |
| Description | 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. |
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