WASS: A Weakness-Aware Subset Selection Framework for Source-Sharing-Constrained Cross-Project Defect Prediction

Abstract

Cross-project defect prediction (CPDP) is a promising solution for new software projects lacking historical data. However, modern privacy/compliance constraints and limited sharing budgets often restrict how many labeled source instances can be transferred across organizations. Existing instance selection strategies typically prioritize “safe” majority instances or rely on random sampling, which can miss boundary-defining defects under very small budgets. To address this, we propose weakness-aware subset selection (WASS), a framework for source-side local curation under a source-sharing budget. WASS combines target-domain similarity, weakness-aware scoring, and class-aware quota allocation to curate a compact transferable subset of source instances. Extensive experiments on 88 directed same-family CPDP pairs with seven diverse classifiers (including transfer component analysis [TCA] + logistic regression [LR] and support vector machine [SVM]) show a clear 1%–5% Golden Window under the evaluated protocol. The experimental protocol also clarifies source-sharing accounting, repeated-run randomness, and family-level variation. Therefore, WASS is a practical, plug-and-play cold-start accelerator for source-sharing-constrained CPDP.