TL;DR
The results indicate that traditional ML models, especially random forest and extra trees, are still very effective for metric-based defect prediction, while DL and multi-modal approaches need to be fed with richer software artifacts to reach their full potential.
This study compared no correction, random oversampling, random undersampling, SMOTE, ADASYN, and class-weighted learning across logistic regression, decision tree, random forest, support vector machine, and neural network classifiers to find accuracy alone is unsuitable for selecting defect predictors.
An intelligent machine learning-based bug prediction framework that uses SMOTE for dataset balancing and feature selection to identify the most relevant software metrics and uses advanced ensemble learning techniques, such as CatBoost, LightGBM, and the Stacking Ensemble model, to improve prediction accuracy.
The analysis indicates that no single model is universally optimal; robust software failure prediction requires dataset-aware preprocessing, leakage-safe validation, imbalance-aware evaluation, and an explicit trade-off among predictive performance, computational efficiency, and interpretability.
The results show that the ensemble techniques are a practical approach to boost the precision of LLMs in the detection of vulnerabilities and suggest that ensemble methods offer great potential in the advancement of software security analysis.
Comparative analysis shows that the developed PSNN-FS, despite its simplicity, achieves strong performance competitive with more complex architectures on the CM1 dataset.
This study aims to improve software defect prediction five publicly available NASA datasets by using Random Forest and Classification Network to achieve higher defect prediction accuracy compared to methods without feature selection (WOFS) and to get matrix problem the authors use Classification Network.
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