CodeFort: Robust Training for Code Generation Models
arxiv(2024)
摘要
Code generation models are not robust to small perturbations, which often
lead to inconsistent and incorrect generations and significantly degrade the
performance of these models. Improving the robustness of code generation models
is crucial to better user experience when these models are deployed in
real-world applications. However, existing efforts have not addressed this
issue for code generation models. To fill this gap, we propose CodeFort, a
framework to improve the robustness of code generation models, generalizing a
large variety of code perturbations to enrich the training data and enabling
various robust training strategies, mixing data augmentation, batch
augmentation, adversarial logits pairing, and contrastive learning, all
carefully designed to support high-throughput training. Extensive evaluations
show that we improve the average robust pass rates of baseline CodeGen models
from 14.79 to 21.74. Notably, the improvement in robustness against code-syntax
perturbations is evidenced by a significant decrease in pass rate drop from
95.04
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