28 citations · 48 across the 9 of their papers we have counts for
9 papers
TEMPLATEFUZZ: Fine-Grained Chat Template Fuzzing for Jailbreaking and Red Teaming LLMs
Qingchao Shen, Zibo Xiao, Lili Huang +3
Large Language Models (LLMs) are increasingly deployed across diverse domains, yet their vulnerability to jailbreak attacks, where adversarial inputs bypass safety mechanisms to el…
Optimization-Aware Test Generation for Deep Learning Compilers
Qingchao Shen, Zan Wang, Haoyang Ma +5
Deep Learning (DL) compilers have been widely utilized to optimize DL models for efficient deployment across various hardware. Due to their vital role in the DL ecosystem, ensuring…
Testing Question Answering Software with Context-Driven Question Generation
Shuang Liu, Zhirun Zhang, Jinhao Dong +5
Question-answering software is becoming increasingly integrated into our daily lives, with prominent examples including Apple Siri and Amazon Alexa. Ensuring the quality of such sy…
DESIL: Detecting Silent Bugs in MLIR Compiler Infrastructure
Chenyao Suo, Jianrong Wang, Yongjia Wang +3
MLIR (Multi-Level Intermediate Representation) compiler infrastructure provides an efficient framework for introducing a new abstraction level for programming languages and domain-…
Bounded Exhaustive Random Program Generation for Testing Solidity Compilers
Haoyang Ma, Alastair F. Donaldson, Qingchao Shen +3
By July 2025, smart contracts collectively manage roughly $120 billion in assets. With Solidity remaining the dominant language for smart contract development, the correctness of S…
A Tale of Two DL Cities: When Library Tests Meet Compiler
Qingchao Shen, Yongqiang Tian, Haoyang Ma +5
Deep Learning (DL) compilers typically load a DL model and optimize it with intermediate representation.Existing DL compiler testing techniques mainly focus on model optimization s…
Towards Understanding the Bugs in Solidity Compiler
Haoyang Ma, Wuqi Zhang, Qingchao Shen +3
Solidity compiler plays a key role in enabling the development of smart contract applications on Ethereum by governing the syntax of a domain-specific language called Solidity and…
Fuzzing Deep Learning Compilers with HirGen
Haoyang Ma, Qingchao Shen, Yongqiang Tian +2
Deep Learning (DL) compilers are widely adopted to optimize advanced DL models for efficient deployment on diverse hardware. Their quality has profound effect on the quality of com…
Toward Understanding Deep Learning Framework Bugs
Junjie Chen, Yihua Liang, Qingchao Shen +2
DL frameworks are the basis of constructing all DL programs and models, and thus their bugs could lead to the unexpected behaviors of any DL program or model relying on them. Such…