Software engineering has long sought ways to enhance software testing to ensure that critical software is reliable before deployment. The advent of deep learning (DL) software has disrupted traditional testing and analysis practices, prompting the creation of specialized methods and techniques to address the unique challenges posed by DL. This is particularly crucial in critical systems with safety implications for users and the environment. This presentation will share findings from several years of research on the automation and practical testing of DL models and DL-enabled systems. It will also discuss testing-based safety analysis as an important application of testing.

Lionel C. Briand is professor of software engineering and has shared appointments between (1) The University of Ottawa, Canada and (2) The Lero Centre, University of Limerick, Ireland. In collaboration with colleagues, over 25 years, he has run many collaborative research projects with companies in the automotive, satellite, aerospace, energy, financial, and legal domains. Lionel has held various engineering, academic, and leading positions in seven countries. He currently holds a Canada Research Chair (Tier 1) on “Intelligent Software Dependability and Compliance” and is the director of Lero, the national Irish centre on software research.

Lionel was elevated to the grades of IEEE Fellow and ACM Fellow for his work on software testing and verification. He was granted the IEEE Computer Society Harlan Mills award, the ACM SIGSOFT outstanding research award, and the IEEE Reliability Society engineer-of-the-year award, respectively in 2012, 2022, and 2013. He further received an ERC Advanced grant in 2016 on the topic of modelling and testing cyber-physical systems, the most prestigious individual research award in the European Union, and was elected a fellow of the Academy of Science, Royal Sociey of Canada in 2023.