collaborators

11 papers

cs.AI2026

Mastermind: Strategy-grounded Learning for Repository-Scale Vulnerability Reproduction

Mingzhe Du, Luu Anh Tuan, Tianyi Wu +4

Repository-level vulnerability reproduction is a demanding software engineering (SE) task: an agent must inspect a codebase, infer the input grammar that reaches a vulnerable path,…

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

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.CE2026

MDAgent2: Large Language Model for Code Generation and Knowledge Q&A in Molecular Dynamics

Zhuofan Shi, Hubao A, Yufei Shao +8

Molecular dynamics (MD) simulations are essential for understanding atomic-scale behaviors in materials science, yet writing LAMMPS scripts remains highly specialized and time-cons…

cs.SE2025

DSCodeBench: A Realistic Benchmark for Data Science Code Generation

Shuyin Ouyang, Dong Huang, Jingwen Guo +3

We introduce DSCodeBench, a new benchmark designed to evaluate large language models (LLMs) on complicated and realistic data science code generation tasks. DSCodeBench consists of…

cs.SE2025

Nexus: Execution-Grounded Multi-Agent Test Oracle Synthesis

Dong Huang, Mingzhe Du, Jie M. Zhang +4

Test oracle generation in non-regression testing is a longstanding challenge in software engineering, where the goal is to produce oracles that can accurately determine whether a f…