Adversarial Robustness for Code

Machine learning and deep learning in particular has been recently used to successfully address many tasks in the domain of code such as finding and fixing bugs, code completion, decompilation, type inference and many others. However, the issue of adversarial robustness of models for code has gone l...

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Bibliographic Details
Published in:arXiv.org 2020-08
Main Authors: Bielik, Pavol, Vechev, Martin
Format: Article
Language:English
Subjects:
Learning
Model accuracy
Representations
Robustness
Online Access:Get full text
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Summary:Machine learning and deep learning in particular has been recently used to successfully address many tasks in the domain of code such as finding and fixing bugs, code completion, decompilation, type inference and many others. However, the issue of adversarial robustness of models for code has gone largely unnoticed. In this work, we explore this issue by: (i) instantiating adversarial attacks for code (a domain with discrete and highly structured inputs), (ii) showing that, similar to other domains, neural models for code are vulnerable to adversarial attacks, and (iii) combining existing and novel techniques to improve robustness while preserving high accuracy.
ISSN:2331-8422