Cross-Project Defect Prediction Based on Feature Fusion and Local Domain Adaptation

Cross-project defect prediction (CPDP) is hindered by distribution shifts between source and target projects, so models that excel in within-project software defect prediction (WPDP) often degrade across projects. We propose FLDP, which couples (i) local subset alignment selecting similar source-target file pairs via three file-level metrics and aligning only those subsets with (ii) sequence-graph feature fusion, where TLSTM encodes token sequences and TGCN encodes AST structure into a unified representation. Across 10 transfers on 7 projects, FLDP consistently outperforms classical and recent CPDP baselines in AUC/F1/MCC. Ablation shows both local alignment and fusion are necessary for the gains, and our analysis of selection metrics offers practical guidance for applying CPDP in heterogeneous settings.