2 citations · 2 across the 14 of their papers we have counts for
13 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…
GramTrans: A Better Code Representation Approach in Code Generation
Zhao Zhang, Qingyuan Liang, Zeyu Sun +6
Code generation has shown great promise in assisting software development. A fundamental yet underexplored question is how the choice of code representation affects model performan…
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 execution efficiency remains overlooked. We…
Contamination Means Overestimation? A Fine-Grained Empirical Study in Code Intelligence
Zhen Yang, Hongyi Lin, Yifan He +7
In recent years, code intelligence has gained increasing importance in the field of automated software engineering. Meanwhile, the widespread adoption of Pretrained Language Models…
Prompt Alchemy: Automatic Prompt Refinement for Enhancing Code Generation
Sixiang Ye, Zeyu Sun, Guoqing Wang +4
Code generation has emerged as a key task to automate software development by converting high-level descriptions into executable code. Large language models (LLMs) excel at this bu…
Automatically Learning a Precise Measurement for Fault Diagnosis Capability of Test Cases
Yifan Zhao, Zeyu Sun, Guoqing Wang +5
Prevalent Fault Localization (FL) techniques rely on tests to localize buggy program elements. Tests could be treated as fuel to further boost FL by providing more debugging inform…
Directional Diffusion-Style Code Editing Pre-training
Qingyuan Liang, Zeyu Sun, Qihao Zhu +6
Code pre-trained models have shown promising effectiveness in various software engineering tasks. Among these tasks, many tasks are related to software evolution and/or code editin…
Condor: A Code Discriminator Integrating General Semantics with Code Details
Qingyuan Liang, Zhao Zhang, Chen Liu +9
LLMs demonstrate significant potential across various software engineering tasks. However, they still face challenges in generating correct code on the first attempt when addressin…
Do Advanced Language Models Eliminate the Need for Prompt Engineering in Software Engineering?
Guoqing Wang, Zeyu Sun, Zhihao Gong +5
Large Language Models (LLMs) have significantly advanced software engineering (SE) tasks, with prompt engineering techniques enhancing their performance in code-related areas. Howe…
CupCleaner: A Hybrid Data Cleaning Approach for Comment Updating
Qingyuan Liang, Zeyu Sun, Qihao Zhu +4
Comment updating is an emerging task in software evolution that aims to automatically revise source code comments in accordance with code changes. This task plays a vital role in m…