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

2 citations · 2 across the 3 of their papers we have counts for

collaborators

5 papers

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

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.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.SE20242 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…