3 citations · 6 across the 6 of their papers we have counts for
6 papers
Hidden Licensing Risks in the LLMware Ecosystem
Bo Wang, Yueyang Chen, Jieke Shi +5
Large Language Models (LLMs) are increasingly integrated into software systems, giving rise to a new class of systems referred to as LLMware. Beyond traditional source-code compone…
RepoMark: A Data-Usage Auditing Framework for Code Large Language Models
Wenjie Qu, Yuguang Zhou, Bo Wang +4
The rapid development of Large Language Models (LLMs) for code generation has transformed software development by automating coding tasks with unprecedented efficiency. However, th…
Mut4All: Fuzzing Compilers via LLM-Synthesized Mutators Learned from Bug Reports
Bo Wang, Pengyang Wang, Chong Chen +9
Mutation-based fuzzing is effective for uncovering compiler bugs, but designing high-quality mutators for modern languages with complex constructs (e.g., templates, macros) remains…
RustMap: Towards Project-Scale C-to-Rust Migration via Program Analysis and LLM
Xuemeng Cai, Jiakun Liu, Xiping Huang +6
Migrating existing C programs into Rust is increasingly desired, as Rust offers superior memory safety while maintaining C's high performance. However, vastly different features be…
LLMs are Bug Replicators: An Empirical Study on LLMs' Capability in Completing Bug-prone Code
Liwei Guo, Sixiang Ye, Zeyu Sun +6
Large Language Models (LLMs) have demonstrated remarkable performance in code completion. However, the training data used to develop these models often contain a significant amount…
A Comprehensive Study on Large Language Models for Mutation Testing
Bo Wang, Mingda Chen, Ming Deng +4
Large Language Models (LLMs) have recently been used to generate mutants in both research work and in industrial practice. However, there has been no comprehensive empirical study…