220 citations · 1k across the 53 of their papers we have counts for
7 papers · 1 filter
Active Code Learning: Benchmarking Sample-Efficient Training of Code Models
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
The costly human effort required to prepare the training data of machine learning (ML) models hinders their practical development and usage in software engineering (ML4Code), espec…
A Black-Box Attack on Code Models via Representation Nearest Neighbor Search
Jie Zhang, Wei Ma, Qiang Hu +4
Existing methods for generating adversarial code examples face several challenges: limted availability of substitute variables, high verification costs for these substitutes, and t…
Going Further: Flatness at the Rescue of Early Stopping for Adversarial Example Transferability
Martin Gubri, Maxime Cordy, Yves Le Traon
Transferability is the property of adversarial examples to be misclassified by other models than the surrogate model for which they were crafted. Previous research has shown that e…
Vulnerability Mimicking Mutants
Aayush Garg, Renzo Degiovanni, Mike Papadakis +1
With the increasing release of powerful language models trained on large code corpus (e.g. CodeBERT was trained on 6.4 million programs), a new family of mutation testing tools has…
Boosting Source Code Learning with Text-Oriented Data Augmentation: An Empirical Study
Zeming Dong, Qiang Hu, Yuejun Guo +5
Recent studies have demonstrated remarkable advancements in source code learning, which applies deep neural networks (DNNs) to tackle various software engineering tasks. Similar to…
GAT: Guided Adversarial Training with Pareto-optimal Auxiliary Tasks
Salah Ghamizi, Jingfeng Zhang, Maxime Cordy +3
While leveraging additional training data is well established to improve adversarial robustness, it incurs the unavoidable cost of data collection and the heavy computation to trai…