5 citations · 17 across the 8 of their papers we have counts for
6 papers · 1 filter
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…
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…