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Found 4 papers, 2 papers with code
CLAWSAT: Towards Both Robust and Accurate Code Models
We integrate contrastive learning (CL) with adversarial learning to co-optimize the robustness and accuracy of code models.
Representations of Computer Programs in the Human Brain
We find that the Multiple Demand system, a system of brain regions previously shown to respond to code, contains information about multiple specific code properties, as well as machine learned representations of code.
Generating Adversarial Computer Programs using Optimized Obfuscations
We further show that our formulation is better at training models that are robust to adversarial attacks.
Dependency-Based Neural Representations for Classifying Lines of Programs
We investigate the problem of classifying a line of program as containing a vulnerability or not using machine learning.
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