2 citations · 2 across the 14 of their papers we have counts for
4 papers · 1 filter
GapForge: Directed Compiler Fuzzing via Coverage-Gap Analysis
Mingxuan Zhu, Qingyuan Liang, Junjie Chen +2
Modern compiler codebases (e.g., GCC and LLVM) are large and complex, making comprehensive coverage across diverse code regions highly challenging. Most existing test generation te…
Contextualized Code Pretraining for Code Generation
Chen Liu, Qingyuan Liang, Hanwen Zhang +3
As code generation becomes increasingly central to improving software development efficiency, modern code models are largely trained and evaluated on code with natural-language des…
TRACE: Evaluating Execution Efficiency of LLM-Based Code Translation
Zhihao Gong, Zeyu Sun, Dong Huang +3
While Large Language Models (LLMs) have substantially improved the functional correctness of code translation, the critical dimension of \textit{execution efficiency} remains overl…
Toward Functional and Non-Functional Evaluation of Application-Level Code Generation
Ruwei Pan, Yakun Zhang, Qingyuan Liang +4
Large language models (LLMs) have achieved strong performance on code generation. However, most prior evaluations focus on snippet-level outputs, such as function generation or rep…