Exploiting Assumptions for Effective Monitoring of Real-Time Properties under Partial Observability

Abstract

Runtime verification of temporal properties is essential for ensuring the correctness and reliability of real-time systems, particularly in cyber-physical systems. A significant challenge in this domain is the effective prediction of property failure or success, especially when dealing with partially observable systems. This paper addresses these challenges by developing an Assumption-Based Runtime Verification (ABRV) approach for a continuous real-time setting. Our method exploits assumptions about the system's behavior, specified as Timed Automata, to enable monitors to predict future outcomes and handle unobservable system parts, such as internal faults. Properties to be monitored are specified using Metric Interval Temporal Logic (MITL). The approach also includes formalizing observations with data and time uncertainty using sequences of timed constraints. We present a zone-based online algorithm for computing the monitoring verdict, implemented on top of the UPPAAL tool. Experimental evaluation on proof-of-concept cases demonstrates the approach's feasibility and effectiveness, illustrating how assumptions facilitate earlier verdicts, enable monitoring of properties dependent on unobservable events, and provide insights into scalability.