Song Wang

Bio

Song Wang obtained his Ph.D. degree from the University of Waterloo in Jan. 2019. He is currently serving as an Associate Editor of ACM Transactions on Software Engineering and Methodology (TOSEM). He works at the intersection of Software Engineering and Artificial Intelligence. More specifically, his research focuses on 1) taking the advantages of AI technologies to address challenges of software reliability practice (AI for SE) and 2) developing software reliability assurance techniques to enhance the reliability of AI systems (SE for AI). Recently, with the rise of Large Language Models (LLMs), he has been exploring their potential to optimize and reshape software testing, including mobile testing, fuzz testing, functional testing, etc. (LLM for Testing). So far, he has published over 60 papers in prestigious IEEE/ACM Software Engineering journals and flagship conferences, with over 2,600 citations. His work has received four best paper awards, including 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.