TLSS_ICFS: A novel two-stage cross-project defect prediction approach

Enhancing Cross-Project Defect Prediction via Transfer Component Analysis and Hybrid Ensembles

The findings indicate that alignment parameter selection substantially influences CPDP performance and that a fixed global default is inadequate.

A Robust Multi-Method Feature Selection Framework for Early Software Defect Prediction: Cross-Dataset Evidence from PROMISE Repository

The proposed multi-method feature selection framework shows good feature stability and has a high F1-score in multiple datasets with a value up to 0.42, which means it is effective at cross-dataset defect prediction, and highlights the importance of embedding imbalance-aware learning techniques.

An Explainable Feature Selection and Stacking Ensemble Framework for Software Fault Prediction

Overall, the findings indicate that integrating principled feature selection with a boosting-based stacking ensemble can improve software fault prediction performance while providing greater transparency for software quality management.

TabKANet for Software Defect Prediction: An Oversampling and Feature-Selection Ablation Study

This study adapts TabKANet to the all-numerical, highly imbalanced SDP setting and empirically evaluates it against established baselines, using a structured ablation in order to isolate the contribution of oversampling and feature selection rather than to propose a new architecture.

Cross Project Software Defect Prediction Using Machine Learning with Optimized Feature Selection

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.

Adaptive Just-in-Time software defect prediction using unsupervised concept drift detection