6 citations · 7 across the 5 of their papers we have counts for
5 papers
Learning Generalizable Multimodal Representations for Software Vulnerability Detection
Zeming Dong, Yuejun Guo, Qiang Hu +5
Source code and its accompanying comments are complementary yet naturally aligned modalities-code encodes structural logic while comments capture developer intent. However, existin…
GenCode: A Generic Data Augmentation Framework for Boosting Deep Learning-Based Code Understanding
Zeming Dong, Qiang Hu, Xiaofei Xie +4
Pre-trained code models lead the era of code intelligence, with multiple models designed with impressive performance. However, one important problem, data augmentation for code dat…
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…
On the Effectiveness of Hybrid Pooling in Mixup-Based Graph Learning for Language Processing
Zeming Dong, Qiang Hu, Zhenya Zhang +5
Graph neural network (GNN)-based graph learning has been popular in natural language and programming language processing, particularly in text and source code classification. Typic…
MIXCODE: Enhancing Code Classification by Mixup-Based Data Augmentation
Zeming Dong, Qiang Hu, Yuejun Guo +5
Inspired by the great success of Deep Neural Networks (DNNs) in natural language processing (NLP), DNNs have been increasingly applied in source code analysis and attracted signifi…