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20232026
most citedDo Advanced Language Models Eliminate the Need for Prompt Engineering in Software Engineering?

2 citations · 2 across the 14 of their papers we have counts for

collaborators

14 papers

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2026

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…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2025

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…

cs.PL2025

Grammar-Based Code Representation: Is It a Worthy Pursuit for LLMs?

Qingyuan Liang, Zhao Zhang, Zeyu Sun +9

Grammar serves as a cornerstone in programming languages and software engineering, providing frameworks to define the syntactic space and program structure. Existing research demon…

cs.SE2025

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…

cs.SE2025

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…

cs.SE2024

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…

cs.SE2024★ 2 cited

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…

cs.SE2023

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…