5 citations · 17 across the 8 of their papers we have counts for
10 papers
LLM-Based Semantic Modeling and Cooperative Evolutionary Fuzzing for Traffic Violation Scenario Generation
Yangyang Liu, Xinyu Li, Yan Xiao +2
Ensuring the safety of autonomous driving systems (ADS) in a cost-effective and efficient manner remains a critical challenge. Existing law-guided scenario generation approaches ar…
BASFuzz: Towards Robustness Evaluation of LLM-based NLP Software via Automated Fuzz Testing
Mingxuan Xiao, Yan Xiao, Shunhui Ji +2
Fuzzing has shown great success in evaluating the robustness of intelligent natural language processing (NLP) software. As large language model (LLM)-based NLP software is widely d…
ABFS: Natural Robustness Testing for LLM-based NLP Software
Mingxuan Xiao, Yan Xiao, Shunhui Ji +3
Owing to the exceptional performance of Large Language Models (LLMs) in Natural Language Processing (NLP) tasks, LLM-based NLP software has rapidly gained traction across various d…
Assessing the Robustness of LLM-based NLP Software via Automated Testing
Mingxuan Xiao, Yan Xiao, Shunhui Ji +3
Benefiting from the advancements in LLMs, NLP software has undergone rapid development. Such software is widely employed in various safety-critical tasks, such as financial sentime…
RITFIS: Robust input testing framework for LLMs-based intelligent software
Mingxuan Xiao, Yan Xiao, Hai Dong +2
The dependence of Natural Language Processing (NLP) intelligent software on Large Language Models (LLMs) is increasingly prominent, underscoring the necessity for robustness testin…
LEAP: Efficient and Automated Test Method for NLP Software
Mingxuan Xiao, Yan Xiao, Hai Dong +2
The widespread adoption of DNNs in NLP software has highlighted the need for robustness. Researchers proposed various automatic testing techniques for adversarial test cases. Howev…
Towards Stealthy Backdoor Attacks against Speech Recognition via Elements of Sound
Hanbo Cai, Pengcheng Zhang, Hai Dong +3
Deep neural networks (DNNs) have been widely and successfully adopted and deployed in various applications of speech recognition. Recently, a few works revealed that these models a…
VSVC: Backdoor attack against Keyword Spotting based on Voiceprint Selection and Voice Conversion
Hanbo Cai, Pengcheng Zhang, Hai Dong +2
Keyword spotting (KWS) based on deep neural networks (DNNs) has achieved massive success in voice control scenarios. However, training of such DNN-based KWS systems often requires…
PBSM: Backdoor attack against Keyword spotting based on pitch boosting and sound masking
Hanbo Cai, Pengcheng Zhang, Hai Dong +2
Keyword spotting (KWS) has been widely used in various speech control scenarios. The training of KWS is usually based on deep neural networks and requires a large amount of data. M…
ADF-GA: Data Flow Criterion Based Test Case Generation for Ethereum Smart Contracts
Pengcheng Zhang, Jianan Yu, Shunhui Ji
Testing is an important technique to improve the quality of Ethereum smart contract programs. However, current work on testing smart contract only focus on static problems of smart…