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@inproceedings{2243840, author = {Rebro, Dominik Arne and Rossi, Bruno and Chren, Stanislav}, address = {Not specified}, booktitle = {The 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23)}, doi = {http://dx.doi.org/10.1145/3555776.3577809}, keywords = {Software Defect ; Software Metrics; Mining Software Repositories; Software Quality}, howpublished = {elektronická verze "online"}, language = {eng}, location = {Not specified}, isbn = {978-1-4503-9517-5}, pages = {1469-1472}, publisher = {Association for Computing Machinery (ACM)}, title = {Source Code Metrics for Software Defects Prediction}, year = {2023} }
TY - JOUR ID - 2243840 AU - Rebro, Dominik Arne - Rossi, Bruno - Chren, Stanislav PY - 2023 TI - Source Code Metrics for Software Defects Prediction PB - Association for Computing Machinery (ACM) CY - Not specified SN - 9781450395175 KW - Software Defect KW - Software Metrics KW - Mining Software Repositories KW - Software Quality N2 - 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. ER -
REBRO, Dominik Arne, Bruno ROSSI a Stanislav CHREN. Source Code Metrics for Software Defects Prediction. Online. In \textit{The 38th ACM/SIGAPP Symposium on Applied Computing (SAC '23)}. Not specified: Association for Computing Machinery (ACM), 2023, s.~1469-1472. ISBN~978-1-4503-9517-5. Dostupné z: https://dx.doi.org/10.1145/3555776.3577809.
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