Joint feature representation learning and progressive distribution matching for cross-project defect prediction

•This paper proposed a novel CPDP method named Joint Feature Representation with Double Marginalized Denoising Autoencoders (DMDA-JFR).•Our method mainly includes two parts: joint feature representation learning and progressive distribution matching.•We utilize two novel autoencoders to jointly learn the global and local feature representations simultaneously.•We introduce a repetitious pseudo-labels strategy to progressively match distribution.