Balanced Adversarial Tight Matching for Cross-Project Defect Prediction

Abstract

Cross-project defect prediction (CPDP) is an attractive research area in software testing. It identifies defects in projects with limited labeled data (target projects) by utilizing predictive models from data-rich projects (source projects). Existing CPDP methods based on transfer learning mainly rely on the assumption of a unimodal distribution and consider the case where the feature distribution has one obvious peak. However, in actual situations, the feature distribution of project samples often exhibits multiple peaks that cannot be ignored. It manifests as a multimodal distribution, making it challenging to align distributions between different projects. To address this issue, we propose a balanced adversarial tight-matching model for CPDP. Specifically, this method employs multilinear conditioning to obtain the cross-covariance of both features and classifier predictions, capturing the multimodal distribution of the feature. When reducing the captured multimodal distribution differences, pseudo-labels are needed, but pseudo-labels have uncertainty. Therefore, we additionally add an auxiliary classifier and attempt to generate pseudo-labels using a pseudo-label strategy with less uncertainty. Finally, the feature generator and two classifiers undergo adversarial training to align the multimodal distributions of different projects. This method outperforms the state-of-the-art CPDP model used on the benchmark dataset.