Symbolic runtime verification for monitoring under uncertainties and assumptions

Runtime verification (RV) examines whether a system’s run satisfies its specification. This typically requires full knowledge of the run, but many applications face imprecise or missing inputs, e.g., from noisy sensors. We aim to develop a symbolic RV procedure that can handle noisy inputs and is able to exploit assumptions that encode background knowledge about the system in order to produce reliable monitoring verdicts. As the symbolic setting in general induces increasingly large monitoring states, we aim to identify fragments of specifications where monitoring requires constant memory. After providing a formalization of the problem at hand, we propose an RV procedure and give formal correctness statements and proofs. We empirically validate our approach in two realistic case studies. The developed RV procedure is the first to effectively handle both uncertainties and assumptions in the expressive setting of Lola, and we identify relevant and expressive fragments where our procedure requires constant memory. Our evaluation witnesses the practical applicability of the approach. RV with uncertainties and assumptions is feasible in the Lola setting, and needs only constant memory in some relevant fragments. Future work will explore further theories and adapt the approach to specific applications.