1 citations · 1 across the 2 of their papers we have counts for
6 papers
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…