Direct Transfer Learning for Cross-Project Test Case Prioritization under Cold-Start Conditions

Abstract

Test Case Prioritization (TCP) aims to reorder test cases to enable earlier fault detection in Continuous Integration pipelines. Machine learning-based TCP approaches have shown promising results but require substantial historical execution data, creating a cold-start challenge for new or rapidly evolving projects. This paper presents an empirical study of direct transfer learning, where models trained on data-rich source projects are applied to unseen targets without any retraining. A total of 578 experiments are conducted across 17 Defects4J projects (34 within-project baselines and 544 cross-project transfer scenarios) using 270,611 feature vectors. Results show that with appropriate source selection, cross-project transfer can match or surpass within-project baselines. A three-way feature ablation (HIS-only, COV-only, full 13-feature) finds that no feature subset consistently exceeds random ordering (E[APFD]≈0.5) on average, confirming that source selection is the critical variable. Analysis identifies failure-rate alignment and source dataset richness as key predictors of transfer quality, providing source-selection criteria that require no target-side data collection.