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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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