UFR-OSFA: Unified Feature Representation and Oppositional Structure Feature Alignment for Mixed-Project Heterogeneous Defect Prediction

Heterogeneous defect prediction (HDP) plays a crucial role in software engineering by enabling the early detection of software defects across projects with heterogeneous feature spaces. Recently, some mixed-project HDP (MP-HDP) methods have been proposed, which have demonstrated modest improvements in HDP performance. Nevertheless, existing MP-HDP approaches fail to address feature redundancy and distribution inconsistency simultaneously. To overcome these limitations, this paper proposes a novel MP-HDP approach, UFR-OSFA, based on unified feature representation and oppositional structural feature alignment. Concretely, UFR-OSFA first unifies these features by reducing the distribution differences between source and target projects through matching common features and the Hungarian algorithm based on the Kolmogorov–Smirnov (KS) test. Subsequently, utilizing a generator and two classifiers with oppositional structures, UFR-OSFA separates the features of the source project and clusters those of the target project, addressing the issue of conditional distribution mismatch and enhancing the model's generalization ability in the target project. Extensive experiments on 23 projects from five datasets demonstrate that the proposed approach performs better or comparably to baseline methods.