4 citations · 8 across the 6 of their papers we have counts for
7 papers
A Single Patch Is Not Enough: Deterministic Fusion of Repair Candidates
Boyang Yang, Xiangliang Hu, Luyao Ren +4
Modern LLM coding agents are commonly evaluated using pass@k, but developers typically apply a single final patch in real-world settings. This pass@k-to-pass@1 gap is a post-genera…
Input Reduction Enhanced LLM-based Program Repair
Boyang Yang, Luyao Ren, Xin Yin +3
Large Language Models (LLMs) have shown great potential in Automated Program Repair (APR). Test inputs, being crucial for reasoning the root cause of failures, are always included…
Validity-Preserving Delta Debugging via Generator Trace Reduction
Luyao Ren, Xing Zhang, Ziyue Hua +4
Reducing test inputs that trigger bugs is crucial for efficient debugging. Delta debugging is the most popular approach for this purpose. When test inputs need to conform to certai…
Programming by Example Made Easy
Jiarong Wu, Lili Wei, Yanyan Jiang +3
Programming by example (PBE) is an emerging programming paradigm that automatically synthesizes programs specified by user-provided input-output examples. Despite the convenience f…
Reliability Assurance for Deep Neural Network Architectures Against Numerical Defects
Linyi Li, Yuhao Zhang, Luyao Ren +2
With the widespread deployment of deep neural networks (DNNs), ensuring the reliability of DNN-based systems is of great importance. Serious reliability issues such as system failu…
Effective Random Test Generation for Deep Learning Compilers
Luyao Ren, ZiHeng Wang, Yingfei Xiong +3
Deep learning compilers help address the difficulties of deploying deep learning models on diverse types of hardware. Testing deep learning compilers is highly crucial, because the…
GDsmith: Detecting Bugs in Graph Database Engines
Wei Lin, Ziyue Hua, Luyao Ren +3
Graph database engines stand out in the era of big data for their efficiency of modeling and processing linked data. There is a strong need of testing graph database engines. Howev…