TL;DR
This paper highlights that integrating the binary Shuffled Frog Leaping Optimizer with machine learning algorithms significantly improves accuracy, F1 score, precision, and sensitivity in defect prediction.
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.
This study proposes an integrated CPDP framework that combines Particle Swarm Optimization with Domain Knowledge (PSO+DK) for feature selection and Adaptive Synthetic Sampling (ADASYN) for class imbalance handling that enhanced the discriminative power of the models.
The core influence mechanism of dataset category balance and project scale on feature-model adaptability is revealed, and the empirical reference for defect prediction practice of different types of software projects is provided.
Software defect prediction (SDP) is essential for improving software quality since it finds error-prone modules early in the development lifecycle. Current methods produce inflated and erroneous performance metrics because of data leaks, inadequate class imbalance management, and reliance on antiquated classifiers. By...
The results demonstrate that ensemble methods provide superior performance in identifying reliability levels, and which classification method is most suitable for predicting software reliability based on code metrics such as Cyclomatic Complexity and Halstead Volume.
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