2 citations · 4 across the 11 of their papers we have counts for
14 papers
Last Secure Code Benchmark: Is the Code You Vibe Secure?
Tianyi Wu, Chao Ge, Peng Chen +12
AI coding agents now write much of the code that ships, yet whether that code is secure remains unclear. We introduce the Last Secure Code Benchmark, a comprehensive evaluation of…
Collective Bias Mitigation via Model Routing and Collaboration
Mingzhe Du, Luu Anh Tuan, Xiaobao Wu +7
Large language models (LLMs) are increasingly deployed in public health, finance, and governance, requiring both accuracy and societal value alignment. Despite recent advances, LLM…
Measuring the Checker: Mutation Analysis for GPU-Kernel Benchmark Oracles
Mingzhe Du, Anh Tuan Luu, Dong Huang +1
Benchmarks for LLM-generated GPU kernels decide correctness with a few random inputs and a loose floating-point tolerance, and their verdicts now feed leaderboards and reinforcemen…
Disorder-induced conducting edges on Kagomé lattice
A. Chmeruk, D. Jones, L. Chioncel
Within a cluster extension of the coherent potential approximation, disorder averaging generates a non-local self-energy that renormalizes both diagonal and off-diagonal hopping te…
MatReplace: A Reference-Free, Conditioning-Aligned Benchmark for Material Replacement in Interior Scenes
Mingzhe Du, Thong Thanh Nguyen, Nguyen Tran Cong Duy +2
Material replacement is a common interior-design operation: changing the material of a selected surface while preserving its geometry, surroundings, and illumination. Despite its c…
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,…
CodeScaler: Scaling Code LLM Training and Test-Time Inference via Reward Models
Xiao Zhu, Xinyu Zhou, Boyu Zhu +5
Reinforcement Learning from Verifiable Rewards (RLVR) has driven recent progress in code large language models by leveraging execution-based feedback from unit tests, but its scala…
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…
Benchmarking LLMs for Unit Test Generation from Real-World Functions
Dong Huang, Jie M. Zhang, Mark Harman +3
Recently, large language models (LLMs) have shown great promise in automating unit test generation, significantly reducing the manual effort required by developers. To effectively…
EffiBench-X: A Multi-Language Benchmark for Measuring Efficiency of LLM-Generated Code
Yuhao Qing, Boyu Zhu, Mingzhe Du +9
Existing code generation benchmarks primarily evaluate functional correctness, with limited focus on code efficiency and often restricted to a single language like Python. To addre…
CodeArena: A Collective Evaluation Platform for LLM Code Generation
Mingzhe Du, Anh Tuan Luu, Bin Ji +5
Large Language Models (LLMs) have reshaped code generation by synergizing their exceptional comprehension of natural language and programming syntax, thereby substantially boosting…
Curriculum Demonstration Selection for In-Context Learning
Duc Anh Vu, Nguyen Tran Cong Duy, Xiaobao Wu +4
Large Language Models (LLMs) have shown strong in-context learning (ICL) abilities with a few demonstrations. However, one critical challenge is how to select demonstrations to eli…
AntiLeakBench: Preventing Data Contamination by Automatically Constructing Benchmarks with Updated Real-World Knowledge
Xiaobao Wu, Liangming Pan, Yuxi Xie +7
Data contamination hinders fair LLM evaluation by introducing test data into newer models' training sets. Existing studies solve this challenge by updating benchmarks with newly co…
Measuring the Influence of Incorrect Code on Test Generation
Dong Huang, Jie M. Zhang, Mark Harman +2
It is natural to suppose that a Large Language Model is more likely to generate correct test cases when prompted with correct code under test, compared to incorrect code under test…