CLAWSAT: Towards Both Robust and Accurate Code Models

Understand

We integrate contrastive learning (CL) with adversarial learning to co-optimize the robustness and accuracy of code models.

• Different from existing works, we show that code obfuscation, a standard code transformation operation, provides novel means to generate complementary `views' of a code that enable us to achieve both robust and accurate code models.
• To the best of our knowledge, this is the first systematic study to explore and exploit the robustness and accuracy benefits of (multi-view) code obfuscations in code models.
• Specifically, we first adopt adversarial codes as robustness-promoting views in CL at the self-supervised pre-training phase.