Generating Adversarial Examples for Holding Robustness of Source Code Processing Models

Automated processing, analysis, and generation of source code are among the key activities in software and system lifecycle. To this end, while deep learning (DL) exhibits a certain level of capability in handling these tasks, the current stateof-the-art DL models still suffer from non-robust issues and can be easily fooled by adversarial attacks. Different from adversarial attacks for image, audio, and natural languages, the structured nature of programming languages brings new challenges. In this paper, we propose a Metropolis-Hastings sampling-based identifier renaming technique, named Metropolis-Hastings Modifier (MHM), which generates adversarial examples for DL models specialized for source code processing. Our in-depth evaluation on a functionality classification benchmark demonstrates the effectiveness of MHM in generating adversarial examples of source code. The higher robustness and performance enhanced through our adversarial training with MHM further confirms the usefulness of DL models-based method for future fully automated source code processing.

Authors

• Huangzhao Zhang
• Zhuo Li Shenzhen Research Institute of Big Data, The Chinese University of Hong Kong, Shenzhen, Guangdong, China
• Ge Li Guangdong Provincial Key Laboratory of Ultra High Definition Immersive Media Technology, Shenzhen Graduate School, Peking University
• Lei Ma The University of Tokyo University of Alberta
• Yang Liu College of Computer Science and Software Engineering, Shenzhen University
• Zhi Jin School of Computer Science, Peking University MoE Key Lab. of High Confidence Software Technologies(PKU), China

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