Dr. Tao Zhang

Dr. Tao Zhang is a Full Professor at the School of Computer Science and Engineering, Macau University of Science and Technology (MUST), Macao SAR. He earned his Ph.D. in Computer Science from the University of Seoul, followed by a one-year appointment as a Postdoctoral Research Fellow at The Hong Kong Polytechnic University. He also holds a B.S. in Automation and an M.Eng. in Software Engineering from Northeastern University in China.

Dr. Zhang serves as the Founding Chair of the IEEE Computer Society Macau Chapter and is the Conference Chair for the IEEE Computer Society Technical Committee on Software Engineering (TCSE). He is a Fellow of the British Computer Society (BCS), a Senior Member of both ACM and IEEE, and a Distinguished Member of the China Computer Federation (CCF). He has also been named a Distinguished Visitor of the IEEE Computer Society.

With over 100 publications in leading journals and conferences in the fields of software engineering and security, his work has appeared in venues such as ICSE, ESEC/FSE, ASE, TSE, TOSEM, EMSE, JSS, IST, TIFS, TDSC, and TSC. Dr. Zhang has served as General Chair for numerous academic conferences, including ISSRE 2027, APSEC 2025, and SANER 2023. He also regularly serves as a Program Committee member for top-tier software engineering conferences, including ICSE, FSE, ASE, and ISSTA. In his editorial roles, Dr. Zhang is an Associate Editor-in-Chief of IEEE Transactions on Software Engineering (TSE). He also serves as an Associate Editor for IEEE Transactions on Reliability (TRel), the Journal of Systems and Software (JSS), and the IEEE Open Journal of the Computer Society (OJCS). Additionally, he is an Editorial Board Member of Empirical Software Engineering (EMSE) and Science of Computer Programming (SCP). Dr. Zhang has received several awards for his service, including the Distinguished Reviewer Award from FSE 2026 and TOSEM 2023, and the Top Reviewer Award from JSS 2023 and IST 2020.

Contact: tazhang@must.edu.mo

Web: https://cszhangtao.github.io/

Linkedin: https://www.linkedin.com/in/tao-zhang-25458229/

Abstracts

Frontiers in Intelligent Software Data Analytics

Intelligent Software Data Analytics has long been a focal research topic in software engineering. With the emergence of new AI technologies such as deep learning and large language models (LLMs), these intelligent analysis methods have demonstrated promising results in data analytics tasks across software development and testing processes. However, they also present significant challenges. Our research has evolved from traditional information retrieval and machine learning-based approaches to leveraging advanced techniques like deep learning and LLMs. We have proposed solutions for various software engineering and security challenges, including code search, defect localization, priority prediction, malware detection, and smart contract vulnerability detection, among others. Additionally, we have developed a suite of supporting tools. While these tools have achieved performance improvements in their respective tasks, they have also prompted deeper reflections, such as "Are large models truly a panacea?"—and provided valuable insights for our future work.

How AI is Changing the World of Software Engineering

With the rapid growth of AI, it deeply influences almost all of computer science, especially for software engineering. The software development process generates a large amount of corpus data (such as defect reports, source code, logs, etc.). How to use these corpus data to better implement automated software engineering tasks is a big challenge. The difficulty lies in the semantic gap between natural language and programming language. In this new era, generative AI can help automatically produce more reliable source code, patches, commits, code comments, and responses to user reviews by deeply analyzing the semantic relations between natural language and programming language. For achieving the best performance of automated software engineering tasks, a lot of software engineering scholars walk through a long road. For our team, we started from the initial reliance on bug reports or user review information to perform a single automated software engineering task. By establishing a unified neural network model and a unified representation model for bug reports, we constructed a set of methods that can achieve multiple automated software engineering tasks. In the process, we discovered the over-interpretation problem of pre-trained language models when implementing automated software engineering tasks, and proposed mitigation strategies. Following this way, depending on the huge power of LLMs, we proposed a series of new models and corresponding tools to enhance the performance of automated software engineering tasks.