Mamta Dahiya

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.