1 citations · 1 across the 1 of their papers we have counts for
3 papers
cs.SE2025
Code2Bench: Scaling Source and Rigor for Dynamic Benchmark Construction
Zhe Zhang, Runlin Liu, Aishan Liu +3
The evaluation of code-generating Large Language Models (LLMs) is fundamentally constrained by two intertwined challenges: a reliance on static, easily contaminated problem sources…
cs.CR2025★ 1 cited
SafeGenBench: A Benchmark Framework for Security Vulnerability Detection in LLM-Generated Code
Xinghang Li, Jingzhe Ding, Chao Peng +4
The code generation capabilities of large language models(LLMs) have emerged as a critical dimension in evaluating their overall performance. However, prior research has largely ov…
cs.SE2025
Effective LLM Code Refinement via Property-Oriented and Structurally Minimal Feedback
Lehan He, Zeren Chen, Zhe Zhang +2
LLMs excel at code generation, yet ensuring the functional correctness of their outputs remains a persistent challenge. While recent studies have applied Test-Driven Development (T…