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

2 citations · 2 across the 2 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.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

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.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…