As software is rapidly being embedded into major parts of our society, ranging from medical devices and self-driving vehicles to critical infrastructures, potential risks of software failures are also growing at an alarming pace. Existing certification processes, however, suffer from a lack of rigor and automation, and often incur a significant amount of manual effort on both system developers and certifiers.
To address this issue, we propose a substantially automated, cost-effective certification method, backed with a novel \emph{analysis synthesis} technique to automatically generate application-specific analysis tools that are custom-tailored to producing the necessary evidence. The outcome of this research promises to not only assist software developers in producing safer and more reliable software, but also benefit industrial certification agencies by significantly reducing the manual effort of certifiers. Early validation flows from experience applying this approach in constructing an assurance case for a surgical robot system in collaboration with the Center for the Advanced Surgical Technology.