1 citations · 1 across the 2 of their papers we have counts for
3 papers
cs.SE2025
AI Code in the Wild: Measuring Security Risks and Ecosystem Shifts of AI-Generated Code in Modern Software
Bin Wang, Wenjie Yu, Yilu Zhong +6
Large language models (LLMs) for code generation are becoming integral to modern software development, but their real-world prevalence and security impact remain poorly understood.…
cs.SE2025
RefleXGen:The unexamined code is not worth using
Bin Wang, Hui Li, AoFan Liu +7
Security in code generation remains a pivotal challenge when applying large language models (LLMs). This paper introduces RefleXGen, an innovative method that significantly enhance…
cs.SE2025★ 1 cited
A.S.E: A Repository-Level Benchmark for Evaluating Security in AI-Generated Code
Keke Lian, Bin Wang, Lei Zhang +19
The increasing adoption of large language models (LLMs) in software engineering necessitates rigorous security evaluation of their generated code. However, existing benchmarks ofte…