21 citations · 21 across the 3 of their papers we have counts for
3 papers
cs.SE2026
Chiseling Out Efficiency: Structured Skeleton Supervision for Efficient Code Generation
Yu Yu, Zhihong Sun, Jia Li +8
Large Language Models (LLMs) are capable of generating syntactically correct and functionally complete programs, greatly streamlining software development. However, recent studies…
cs.SE2025
SemGuard: Real-Time Semantic Evaluator for Correcting LLM-Generated Code
Qinglin Wang, Zhihong Sun, Ruyun Wang +4
Large Language Models (LLMs) can translate natural language requirements into code, yet empirical analyses of representative models reveal that semantic errors-programs that compil…
cs.SE2021★ 21 cited
Embedding API Dependency Graph for Neural Code Generation
Chen Lyu, Ruyun Wang, Hongyu Zhang +2
The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approa…