时间：2025年4月17日 10：00
地点：北区杨咏曼楼606
报告内容：A design model serves as an abstract representation of a real-world process or software system. While certain software faults can be detected by analyzing design models prior to implementation, the process of manually repairing these models can be time-consuming for developers. To accelerate software development, this paper presents an automated approach for repairing design models that have been diagnosed using model checking techniques. Model checkers are employed to identify common issues such as unreachable goals and violated system properties. The proposed method addresses these faults in parallel by applying insertion, modification, and deletion operations, which are guided by constraint solving and predictive modeling techniques. The effectiveness of the repairs is evaluated using ISO/IEC 25010 software quality metrics. Experimental results show that the approach successfully resolves unreachable goals and invariant violations in a range of design models, while preserving overall model quality. The performance and effectiveness of the repair process are influenced primarily by the complexity of the design model, the efficiency of the constraint solver, and the accuracy of the predictive model. This study demonstrates that automating model diagnosis, fault correction, and quality evaluation can significantly enhance the efficiency of model-driven software development.
主讲人介绍：Dr. Jing Sun received his PhD in Computer Science from the National University of Singapore in 2004. He subsequently joined the University of Auckland, where he is currently an Associate Professor in the School of Computer Science. His research focuses on AI-driven software engineering, with a strong emphasis on secure software development. In recent years, he has applied generative AI and large language models (LLMs) to enhance the security and quality of automated software systems. Dr. Sun’s work spans several key areas, including machine learning for automated formal design model repair and LLM-based code generation. He has also explored advanced LLM techniques for smart contract auditing - a critical aspect of cybersecurity that addresses vulnerabilities in blockchain systems. Additionally, he is investigating verification methods to ensure the accuracy and reliability of AI-generated outputs, thereby strengthening the integrity of complex software systems. To date, Dr. Sun has published 132 research papers in leading venues, including ACM Transactions on Software Engineering and Methodology, Automated Software Engineering, ACM Computing Surveys, Information Sciences, Information and Software Technology, Expert Systems with Applications, and IEEE Transactions on Reliability. He has also held several leadership roles in the international research community, serving as a conference chair, program chair, and steering committee chair. More details can be found on his university profile homepage at: https://www.cs.auckland.ac.nz/~jingsun/.
