51 citations · 80 across the 12 of their papers we have counts for
14 papers
When is LLM-Based Program Reasoning Correct? A Completion Semantics for LLM-Based Code Inference
Zhiyuan Liu, Yihe Li, Trevor E. Carlson +3
Due to token and cognitive limits, Large Language Models (LLMs) typically perform program reasoning over incomplete code fragments/prompts rather than complete programs. Such reaso…
Persistent Iterators with Value Semantics
Yihe Li, Gregory J. Duck
Iterators are a fundamental programming abstraction for traversing and modifying elements in containers in mainstream imperative languages such as C++. Iterators provide a uniform…
Large Language Model Powered Symbolic Execution
Yihe Li, Ruijie Meng, Gregory J. Duck
Large Language Models (LLMs) have emerged as a promising alternative to traditional static program analysis methods, such as symbolic execution, offering the ability to reason over…
Large Language Model assisted Hybrid Fuzzing
Ruijie Meng, Gregory J. Duck, Abhik Roychoudhury
Greybox fuzzing is one of the most popular methods for detecting software vulnerabilities, which conducts a biased random search within the program input space. To enhance its effe…
Fully Randomized Pointers
Sai Dhawal Phaye, Gregory J. Duck, Roland H. C. Yap +1
Memory errors continue to be a critical concern for programs written in low-level programming languages such as C and C++. Many different memory error defenses have been proposed,…
Program Environment Fuzzing
Ruijie Meng, Gregory J. Duck, Abhik Roychoudhury
Computer programs are not executed in isolation, but rather interact with the execution environment which drives the program behaviors. Software validation methods thus need to cap…