Feature-based Transfer Learning in Cross-Project Defect Prediction: A Systematic Review

This review examines recent feature-based transfer learning techniques for Cross-Project Software Defect Prediction. We summarize representative approaches in five categories-feature selection, feature mapping/alignment, deep/adversarial learning, semantics-enhanced transfer, and hybrid/multi-source designs-and discuss their reported effectiveness across common benchmark datasets. The review highlights trade-offs between predictive accuracy, computational cost, and model interpretability, and concludes with open challenges and directions for future work.