Song Wang

Song Wang is an Associate Professor in the Department of Electrical Engineering and Computer Science at York University's Lassonde School of Engineering. He joined York University as an Assistant Professor in July 2019 and was promoted to Associate Professor in May 2024. He serves as an Associate Editor of ACM Transactions on Software Engineering and Methodology (TOSEM) and has established himself as a prominent researcher at the intersection of Software Engineering and Artificial Intelligence.

Dr. Wang earned his Ph.D. in Computer Engineering from the University of Waterloo in December 2018 under Prof. Lin Tan. He received his MS degree from the Chinese Academy of Sciences in June 2014 under the supervision of Prof. Ye Yang, Prof. Wen Zhang, and Prof. Qing Wang. His undergraduate education includes a BE in Software Engineering and a BHRM in Human Resource Management from Sichuan University in June 2011. Prior to academia, he gained industry experience through internships at Microsoft Research, Morgan Stanley Capital International, Yahoo, and Baidu, and co-founded a startup named QualDivine.

Dr. Wang's research focuses on two main directions: (1) leveraging AI technologies to address software reliability challenges (AI for SE), and (2) developing software reliability assurance techniques for AI systems (SE for AI). His recent work has particularly focused on how Large Language Models can optimize and reshape software testing practices. His research has practical impact, with tools and techniques that have detected hundreds of true bugs in real-world software systems. His work spans multiple application areas including mobile testing, fuzz testing, and functional testing.

His recent publications (2024-2025) demonstrate a strong focus on the intersection of AI and software engineering, with significant contributions in automated vulnerability detection, API recommendation, bias analysis in generated code, and mobile application testing. His research combines empirical studies with innovative technical approaches, often involving benchmarking and systematic literature reviews to establish foundations for future work. He has published over 60 papers in prestigious IEEE/ACM Software Engineering journals and flagship conferences, with over 2,600 citations.

Dr. Wang has received four best paper awards: a Distinguished Paper Award at APSEC'23, an ACM Distinguished Paper Award at ICPC'22, an ACM Distinguished Paper Award at ICSE'20, and a Best Paper Award at PROMISE'19. He was recognized as one of the top-10 most impactful early-career researchers in Software Engineering by the Journal of Systems and Software in 2020 and received the TOSEM Distinguished Reviewer Award in 2023.

Dr. Wang currently supervises multiple PhD and Master's students including Mohammad Abdollahi, Haoran Xue, Jiho Shin, Nima Shiri Harzevili, and Moshi Wei. He has successfully guided several students to complete their theses, including Reem Al Eithan (Master's thesis defense in April 2025), Moshi Wei (PhD thesis defense in April 2025), and Nima Shiri Harzevili (PhD thesis defense in February 2025). His research group has received funding from various sources to support their work on software engineering and AI.

Dr. Wang leads an active research group focused on AI and software engineering at York University. His team includes PhD students, Master's students, and research assistants working on various projects related to software testing, reliability, and AI applications in software engineering. The group has developed tools that have detected hundreds of true bugs in real-world software systems, with some findings documented in Jira issues and GitHub repositories across numerous open-source projects.