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20242026
most citedDo Advanced Language Models Eliminate the Need for Prompt Engineering in Software Engineering?

2 citations · 2 across the 4 of their papers we have counts for

collaborators

5 papers

cs.SE2026

DSEffi-Bench: Demystifying Large Language Models' Capability in Efficient Data Science Code Generation

Zhihao Gong, Junzhe Yu, Dong Huang +3

Current data science (DS) code generation benchmarks equate correctness with quality, overlooking execution time differences that span orders of magnitude between correct solutions…

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.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.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…

cs.CR2024

Improving Smart Contract Security with Contrastive Learning-based Vulnerability Detection

Yizhou Chen, Zeyu Sun, Zhihao Gong +1

Currently, smart contract vulnerabilities (SCVs) have emerged as a major factor threatening the transaction security of blockchain. Existing state-of-the-art methods rely on deep l…