5 citations · 13 across the 7 of their papers we have counts for
8 papers
Merged, Not Measured: An Empirical Study of Performance Issues Fixed by Coding Agents
Zhenyu Qi, Haotang Li, Jinfu Chen +6
Coding agents open pull requests (PRs) that claim to speed up software, but studies of human performance fixes say little about how maintainers respond to such a fix or whether its…
LegacyWorld: Atomicity-Aware Evaluation of GUI Agents for Legacy Workflows
Thilo Reintjes, Sivajeet Chand, Derui Zhu +2
Legacy and legacy-like enterprise systems often remain difficult to modernize because critical workflows expose limited programmable interfaces and still require manual GUI interac…
DepWareTrans: Dependency-Aware Incremental Repository Migration across Co-executable Languages
Sivajeet Chand, Alexander Pretschner, Steve Haupt +2
Repository-level code translation is critical for modernizing legacy systems, yet existing approaches based on large language models (LLMs) operate at the file level and fail to sc…
PoLLMgraph: Unraveling Hallucinations in Large Language Models via State Transition Dynamics
Derui Zhu, Dingfan Chen, Qing Li +4
Despite tremendous advancements in large language models (LLMs) over recent years, a notably urgent challenge for their practical deployment is the phenomenon of hallucination, whe…
LUNA: A Model-Based Universal Analysis Framework for Large Language Models
Da Song, Xuan Xie, Jiayang Song +4
Over the past decade, Artificial Intelligence (AI) has had great success recently and is being used in a wide range of academic and industrial fields. More recently, LLMs have made…
Neural Episodic Control with State Abstraction
Zhuo Li, Derui Zhu, Yujing Hu +6
Existing Deep Reinforcement Learning (DRL) algorithms suffer from sample inefficiency. Generally, episodic control-based approaches are solutions that leverage highly-rewarded past…
Data Forensics in Diffusion Models: A Systematic Analysis of Membership Privacy
Derui Zhu, Dingfan Chen, Jens Grossklags +1
In recent years, diffusion models have achieved tremendous success in the field of image generation, becoming the stateof-the-art technology for AI-based image processing applicati…
Towards Understanding Quality Challenges of the Federated Learning for Neural Networks: A First Look from the Lens of Robustness
Amin Eslami Abyane, Derui Zhu, Roberto Souza +2
Federated learning (FL) is a distributed learning paradigm that preserves users' data privacy while leveraging the entire dataset of all participants. In FL, multiple models are tr…