3 citations · 3 across the 9 of their papers we have counts for
10 papers
What Was Once Learned May Need to Be Unlearned: Machine Unlearning for Deprecated API Knowledge in Large Language Models
Jin Liu, Yanzhong He, Guancheng Lin +4
Large language models (LLMs) for code completion may generate deprecated APIs because their pre-training corpora contain code from historical library versions. Existing approaches…
Teaching Software Engineering with LLM and MCP Integration: From Classroom to Industry Practice
Kehui Chen, Jacky Keung, Weining Li +3
The rapid integration of Large Language Models (LLMs) and the Model Context Protocol (MCP) into industrial software engineering has created a pressing need to update software engin…
Understanding How Enterprises Adopt the Model Context Protocol for LLM-Driven Software Engineering
Kehui Chen, Yicheng Sun, Jacky Keung +2
Large Language Models (LLMs) are increasingly used in AI-based software engineering, but their limitations in complex task execution and multi-tool coordination have driven growing…
UniAda: Universal Adaptive Multi-objective Adversarial Attack for End-to-End Autonomous Driving Systems
Jingyu Zhang, Jacky Wai Keung, Yan Xiao +3
Adversarial attacks play a pivotal role in testing and improving the reliability of deep learning (DL) systems. Existing literature has demonstrated that subtle perturbations to th…
When Models Meet Users: An Empirical Study of Perceptions of General LLMs and Multimodal LLMs on Hugging Face
Yujian Liu, Xiao Yu, Jacky Keung +3
Large language models (LLMs) have rapidly evolved from general-purpose systems to multimodal models capable of processing text, images, and audio. As both general-purpose LLMs (GLL…
A Comprehensive Study of Bugs in Modern Distributed Deep Learning Systems
Xiaoxue Ma, Wanwei Zhan, Jiale Chen +3
In today's data-driven era, deep learning is vital for processing massive datasets, yet single-device training is constrained by computational and memory limits. Distributed deep l…
PerProb: Indirectly Evaluating Memorization in Large Language Models
Yihan Liao, Jacky Keung, Xiaoxue Ma +2
The rapid advancement of Large Language Models (LLMs) has been driven by extensive datasets that may contain sensitive information, raising serious privacy concerns. One notable th…
Exposing and Defending Membership Leakage in Vulnerability Prediction Models
Yihan Liao, Jacky Keung, Xiaoxue Ma +2
Neural models for vulnerability prediction (VP) have achieved impressive performance by learning from large-scale code repositories. However, their susceptibility to Membership Inf…
Practitioners' Expectations on Log Anomaly Detection
Xiaoxue Ma, Yishu Li, Jacky Keung +5
Log anomaly detection has become a common practice for software engineers to analyze software system behavior. Despite significant research efforts in log anomaly detection over th…
Exploring and Unleashing the Power of Large Language Models in Automated Code Translation
Zhen Yang, Fang Liu, Zhongxing Yu +7
Code translation tools (transpilers) are developed for automatic source-to-source translation. Although learning-based transpilers have shown impressive enhancement against rule-ba…