This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).

Cyber-Physical Systems (CPS) sustainably benefit from software upgrades throughout their life cycles. However, as CPS become machine-learning-intensive due to rapidly increasing interactions between CPS and machine learning technologies, two major distinguishing factors associated with machine learning techniques raise significant safety concerns about CPS upgrades which play a critical role in enabling lifetime safety assurance. First, upgrades of machine learning components, which inherently result in system changes, come at significant safety risk for safety-critical CPS due to the vulnerabilities of machine learning techniques. Second, the traditional safe-by-verification upgrade framework, in which upgrades and verification have to be two separate procedures, is no longer valid for machine learning processes that update instantaneously during system operations. This project targets these unique challenges by developing scalable verification and monitoring methods for upgrades as well as safe upgrade procedures to enable trustworthy upgrades and achieve lifetime safety assurance in machine-learning-intensive CPS.