6 citations · 28 across the 17 of their papers we have counts for
17 papers
Robustness and Trade-offs for Code LLMs on Protected Code
Jin Wen, Yuejun Guo, Yujie Ma +2
Code large language models (LLMs) are increasingly used on software artifacts that may be intentionally obfuscated for intellectual-property protection, reverse-engineering resista…
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…
An Empirical Study of the Imbalance Issue in Software Vulnerability Detection
Yuejun Guo, Qiang Hu, Qiang Tang +1
Vulnerability detection is crucial to protect software security. Nowadays, deep learning (DL) is the most promising technique to automate this detection task, leveraging its superi…
Outside the Comfort Zone: Analysing LLM Capabilities in Software Vulnerability Detection
Yuejun Guo, Constantinos Patsakis, Qiang Hu +2
The significant increase in software production driven by automation and faster development lifecycles has resulted in a corresponding surge in software vulnerabilities. In paralle…
Evaluation and Improvement of Fault Detection for Large Language Models
Qiang Hu, Jin Wen, Maxime Cordy +4
Large language models (LLMs) have recently achieved significant success across various application domains, garnering substantial attention from different communities. Unfortunatel…
Evaluating the Robustness of Test Selection Methods for Deep Neural Networks
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
Testing deep learning-based systems is crucial but challenging due to the required time and labor for labeling collected raw data. To alleviate the labeling effort, multiple test s…
CodeLens: An Interactive Tool for Visualizing Code Representations
Yuejun Guo, Seifeddine Bettaieb, Qiang Hu +2
Representing source code in a generic input format is crucial to automate software engineering tasks, e.g., applying machine learning algorithms to extract information. Visualizing…
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…
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…
Aries: Efficient Testing of Deep Neural Networks via Labeling-Free Accuracy Estimation
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
Deep learning (DL) plays a more and more important role in our daily life due to its competitive performance in industrial application domains. As the core of DL-enabled systems, d…
CodeS: Towards Code Model Generalization Under Distribution Shift
Qiang Hu, Yuejun Guo, Xiaofei Xie +4
Distribution shift has been a longstanding challenge for the reliable deployment of deep learning (DL) models due to unexpected accuracy degradation. Although DL has been becoming…
LaF: Labeling-Free Model Selection for Automated Deep Neural Network Reusing
Qiang Hu, Yuejun Guo, Maxime Cordy +3
Applying deep learning to science is a new trend in recent years which leads DL engineering to become an important problem. Although training data preparation, model architecture d…
Robust Active Learning: Sample-Efficient Training of Robust Deep Learning Models
Yuejun Guo, Qiang Hu, Maxime Cordy +2
Active learning is an established technique to reduce the labeling cost to build high-quality machine learning models. A core component of active learning is the acquisition functi…
MUTEN: Boosting Gradient-Based Adversarial Attacks via Mutant-Based Ensembles
Yuejun Guo, Qiang Hu, Maxime Cordy +2
Deep Neural Networks (DNNs) are vulnerable to adversarial examples, which causes serious threats to security-critical applications. This motivated much research on providing mechan…
Nearly-tight bounds on linear regions of piecewise linear neural networks
Qiang Hu, Hao Zhang
The developments of deep neural networks (DNN) in recent years have ushered a brand new era of artificial intelligence. DNNs are proved to be excellent in solving very complex prob…