Lionel C. Briand is professor of software engineering and has shared appointments between (1) The School of Electrical Engineering and Computer Science, University of Ottawa, Canada and (2) The SnT centre for Security, Reliability, and Trust, University of Luxembourg. He holds a Canada Research Chair in Intelligent Software Dependability and Compliance (Tier 1). He has conducted applied research in collaboration with industry for more than 25 years, including projects in the automotive, aerospace, manufacturing, financial, and energy domains. He is was elevated to the rank of fellow of both the IEEE and ACM. He was also granted the IEEE Computer Society Harlan Mills award (2012), the IEEE Reliability Society Engineer-of-the-year award (2013), and the ACM SIGSOFT Outstanding Research Award (2022). More information can be found on: http://www.lbriand.info
Trustworthy Machine Learning-Enabled Systems
This talk will provide a personal perspective on the state of art regarding the automated testing and analysis of software systems enabled by machine learning. Such systems typically contain components relying on machine learning, whose behavior is not specified or coded but driven by training data, but which interact with other components in the system and play a critical role. Typical examples include cyber-physical systems that rely on machine learning in their perception (e.g., analyzing camera images) and control (e.g., sending commands to actuators) layers. In my reflections, I will rely both on my analysis of the state of the art and personal experience in research projects carried out with industrial partners in the automotive domain.