180 citations · 951 across the 62 of their papers we have counts for
11 papers · 1 filter
GPTScan: Detecting Logic Vulnerabilities in Smart Contracts by Combining GPT with Program Analysis
Yuqiang Sun, Daoyuan Wu, Yue Xue +5
Smart contracts are prone to various vulnerabilities, leading to substantial financial losses over time. Current analysis tools mainly target vulnerabilities with fixed control or…
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…
BehAVExplor: Behavior Diversity Guided Testing for Autonomous Driving Systems
Mingfei Cheng, Yuan Zhou, Xiaofei Xie
Testing Autonomous Driving Systems (ADSs) is a critical task for ensuring the reliability and safety of autonomous vehicles. Existing methods mainly focus on searching for safety v…
How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation
Cen Zhang, Yaowen Zheng, Mingqiang Bai +6
LLM-based (Large Language Model) fuzz driver generation is a promising research area. Unlike traditional program analysis-based method, this text-based approach is more general and…
Multi-target Backdoor Attacks for Code Pre-trained Models
Yanzhou Li, Shangqing Liu, Kangjie Chen +3
Backdoor attacks for neural code models have gained considerable attention due to the advancement of code intelligence. However, most existing works insert triggers into task-speci…
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…
Evading DeepFake Detectors via Adversarial Statistical Consistency
Yang Hou, Qing Guo, Yihao Huang +3
In recent years, as various realistic face forgery techniques known as DeepFake improves by leaps and bounds,more and more DeepFake detection techniques have been proposed. These m…
Evaluating AIGC Detectors on Code Content
Jian Wang, Shangqing Liu, Xiaofei Xie +1
Artificial Intelligence Generated Content (AIGC) has garnered considerable attention for its impressive performance, with ChatGPT emerging as a leading AIGC model that produces hig…
Neural Episodic Control with State Abstraction
Zhuo Li, Derui Zhu, Yujing Hu +6
Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency. Generally, episodic control-based approaches are solutions that leverage highly-rewarded past…
ContraBERT: Enhancing Code Pre-trained Models via Contrastive Learning
Shangqing Liu, Bozhi Wu, Xiaofei Xie +2
Large-scale pre-trained models such as CodeBERT, GraphCodeBERT have earned widespread attention from both academia and industry. Attributed to the superior ability in code represen…