93 citations · 385 across the 72 of their papers we have counts for
74 papers
A Reference Architecture for Integrating Agentic AI into Enterprise Software: Responsibilities, Interactions, and Variability
Mingqin Yu, Fethi Rabhi, Liming Zhu +1
Agentic AI moves generative AI applications toward systems that coordinate reasoning, enterprise context, model inference, and external actions. Integrating these capabilities into…
AI Safety: Not Optional, Not Later
Qinghua Lu, Yoshua Bengio
Incidents show that AI safety failures often arise across multiple layers. We present a safety-by-design assurance architecture combining model-level supervision, such as Scientist…
Harnessing Agent Skills: Architectural Patterns and a Reference Architecture for Skill-Mediated LLM Agents
Boming Xia, Liming Zhu, Zhenchang Xing +3
Agent skills externalise reusable agent-facing behavioural knowledge and guidance as persistent artefacts that can be discovered, activated, and interpreted by LLM agents. Although…
Uncertainty Propagation in LLM-Based Systems
Boming Xia, Liming Zhu, Erdun Gao +3
Uncertainty in large language model (LLM)-based systems is often studied at the level of a single model output, yet deployed LLM applications are compound systems in which uncertai…
Still Manual? Automated Linter Configuration via DSL-Based LLM Compilation of Coding Standards
Zejun Zhang, Yixin Gan, Zhenchang Xing +5
Coding standards are essential for maintaining consistent and high-quality code across teams and projects. Linters help developers enforce these standards by detecting code violati…
A Structured Approach to Safety Case Construction for AI Systems
Sung Une Lee, Liming Zhu, Md Shamsujjoha +4
Safety cases, structured arguments that a system is acceptably safe, are becoming central to the governance of AI systems. Yet, traditional safety-case practices from aviation or n…
DOCUEVAL: An LLM-based AI Engineering Tool for Building Customisable Document Evaluation Workflows
Hao Zhang, Qinghua Lu, Liming Zhu
Foundation models, such as large language models (LLMs), have the potential to streamline evaluation workflows and improve their performance. However, practical adoption faces chal…
MARIA: A Framework for Marginal Risk Assessment without Ground Truth in AI Systems
Jieshan Chen, Suyu Ma, Qinghua Lu +2
Before deploying an AI system to replace an existing process, it must be compared with the incumbent to ensure improvement without added risk. Traditional evaluation relies on grou…
AgentArcEval: An Architecture Evaluation Method for Foundation Model based Agents
Qinghua Lu, Dehai Zhao, Yue Liu +6
The emergence of foundation models (FMs) has enabled the development of highly capable and autonomous agents, unlocking new application opportunities across a wide range of domains…
QUT: A Unit Testing Framework for Quantum Subroutines
Mykhailo V. Klymenko, Thong Hoang, Hoa Nguyen +6
We present the architectural design and prototype implementation of QUT (Quantum Unit Testing), a framework for unit testing of quantum subroutines. The framework is developed with…
SHIELDA: Structured Handling of Exceptions in LLM-Driven Agentic Workflows
Jingwen Zhou, Jieshan Chen, Qinghua Lu +2
Large Language Model (LLM) agentic systems are software systems powered by LLMs that autonomously reason, plan, and execute multi-step workflows to achieve human goals, rather than…
PTMPicker: Facilitating Efficient Pretrained Model Selection for Application Developers
Pei Liu, Terry Zhuo, Jiawei Deng +4
The rapid emergence of pretrained models (PTMs) has attracted significant attention from both Deep Learning (DL) researchers and downstream application developers. However, selecti…
Context-Aware Unit Testing for Quantum Subroutines
Mykhailo Klymenko, Thong Hoang, Samuel A. Wilkinson +6
Software testing is a critical component of the classical software development lifecycle, and this principle is expected to hold true for quantum software as it evolves toward larg…
RAGOps: Operating and Managing Retrieval-Augmented Generation Pipelines
Xiwei Xu, Hans Weytjens, Dawen Zhang +3
Recent studies show that 60% of LLM-based compound systems in enterprise environments leverage some form of retrieval-augmented generation (RAG), which enhances the relevance and a…
SeeAction: Towards Reverse Engineering How-What-Where of HCI Actions from Screencasts for UI Automation
Dehai Zhao, Zhenchang Xing, Qinghua Lu +2
UI automation is a useful technique for UI testing, bug reproduction, and robotic process automation. Recording user actions with an application assists rapid development of UI aut…
FactFlow: Automatic Fact Sheet Generation and Customization from Tabular Dataset via AI Chain Design & Implementation
Minh Duc Vu, Jieshan Chen, Zhenchang Xing +3
With the proliferation of data across various domains, there is a critical demand for tools that enable non-experts to derive meaningful insights without deep data analysis skills.…
An Extended Pattern Collection for Blockchain-based Applications
Xiwei Xu, Cesare Pautasso, Sin Kuang Lo +3
Blockchain is an emerging technology that enables new forms of decentralized software architectures, where distributed components can reach agreements on shared system states witho…
Architectural Patterns for Designing Quantum Artificial Intelligence Systems
Mykhailo Klymenko, Thong Hoang, Xiwei Xu +4
Utilising quantum computing technology to enhance artificial intelligence systems is expected to improve training and inference times, increase robustness against noise and adversa…
DesignRepair: Dual-Stream Design Guideline-Aware Frontend Repair with Large Language Models
Mingyue Yuan, Jieshan Chen, Zhenchang Xing +6
The rise of Large Language Models (LLMs) has streamlined frontend interface creation through tools like Vercel's V0, yet surfaced challenges in design quality (e.g., accessibility,…
Automated Soap Opera Testing Directed by LLMs and Scenario Knowledge: Feasibility, Challenges, and Road Ahead
Yanqi Su, Zhenchang Xing, Chong Wang +4
Exploratory testing (ET) harnesses tester's knowledge, creativity, and experience to create varying tests that uncover unexpected bugs from the end-user's perspective. Although ET…
AgentOps: Enabling Observability of LLM Agents
Liming Dong, Qinghua Lu, Liming Zhu
Large language model (LLM) agents have demonstrated remarkable capabilities across various domains, gaining extensive attention from academia and industry. However, these agents ra…
From Exploration to Revelation: Detecting Dark Patterns in Mobile Apps
Jieshan Chen, Zhen Wang, Jiamou Sun +5
Mobile apps are essential in daily life but frequently employ deceptive patterns, such as visual emphasis or linguistic nudging, to manipulate user behavior. Existing research larg…
Evaluation-Driven Development and Operations of LLM Agents: A Process Model and Reference Architecture
Boming Xia, Qinghua Lu, Liming Zhu +3
Large Language Models (LLMs) have enabled the emergence of LLM agents, systems capable of pursuing under-specified goals and adapting after deployment. Evaluating such agents is ch…
"I Don't Use AI for Everything": Exploring Utility, Attitude, and Responsibility of AI-empowered Tools in Software Development
Shidong Pan, Litian Wang, Tianyi Zhang +4
AI-empowered tools have emerged as a transformative force, fundamentally reshaping the software development industry and promising far-reaching impacts across diverse sectors. This…