Abstract As modern social coding platforms such as GitHub and Stack Overflflow become increasingly popular, their potential security risks increase as well (e.g., risky or malicious codes could be easily embedded and distributed). To enhance the social coding security, in this paper, we propose to automate cross-platform user identifification between GitHub and Stack Overflflow to combat the attackers who attempt to poison the modern software programming ecosystem. To solve this problem, an important insight brought by this work is to leverage social coding properties in addition to user attributes for cross-platform user identifification. To depict users in GitHub and Stack Overflflow (attached with attributed information), projects, questions and answers as well as the rich semantic relations among them, we fifirst introduce an attributed heterogeneous information network (AHIN) for modeling. Then, we propose a novel AHIN representation learning model AHIN2Vec to effificiently learn node (i.e., user) representations in AHIN for cross-platform user identifification. Comprehensive experiments on the data collections from GitHub and Stack Overflflow are conducted to validate the effectiveness of our developed system iDev integrating our proposed method in cross-platform user identifification by comparisons with other baselines