Although a majority of issues are introduced in the early phases of requirement specification and system design, a suitable methodology and proper tooling support for automated formalization and analysis of high-level requirement specifications is still not satisfactory despite various steps in this direction. Despite the recent advances in automated synthesis based on formal methods as well as machine learning techniques, there still exists a principal gap between the limited scalability of automated synthesis methods and the lack of safety and security guarantees provided by machine learning methods. Within this R&I area, we will primarily focus on two areas where model-based design, analysis, and synthesis can improve software engineering processes: i) methodology and tools supporting automation in the requirement specification and ii) automated decision making under uncertainty.
