iDev: Enhancing Social Coding Security by Cross-platform User Identification Between GitHub and Stack Overflow

As modern social coding platforms such as GitHub and Stack Overflow 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 identification between GitHub and Stack Overflow 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 identification. To depict users in GitHub and Stack Overflow (attached with attributed information), projects, questions and answers as well as the rich semantic relations among them, we first introduce an attributed heterogeneous information network (AHIN) for modeling. Then, we propose a novel AHIN representation learning model AHIN2Vec to efficiently learn node (i. e. , user) representations in AHIN for cross-platform user identification. Comprehensive experiments on the data collections from GitHub and Stack Overflow are conducted to validate the effectiveness of our developed system iDev integrating our proposed method in cross-platform user identification by comparisons with other baselines.

Authors

• Yujie Fan Case Western Reserve University, Cleveland, OH 44106
• Yiming Zhang College of Design and Innovation, Tongji University
• Shifu Hou Lucy Family Institute for Data & Society University of Notre Dame
• Lingwei Chen Wright State University
• Yanfang Ye University of Notre Dame Lucy Family Institute for Data & Society
• Chuan Shi Beijing University of Posts and Telecommunications
• Liang Zhao
• Shouhuai Xu

Keywords

• Machine Learning: Data Mining
• Multidisciplinary Topics and Applications: Security and Privacy