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

2024-09-09

IEEE International Conference on Software Engineering and Formal Methods

AI Summary

Runtime verification of temporal properties—such as those expressed in Metric Interval Temporal Logic (MITL)—is challenging in partially observable real-time systems, particularly when critical internal events (e.g., latent faults) remain unobservable. Method: This paper proposes an active prediction approach grounded in prior formal assumptions: system behavior is modeled as a timed automaton and integrated into a runtime verification framework to enable online inference of unobservable internal events. For the first time, formal system assumptions are deeply embedded into the real-time monitoring pipeline, combining constraint-driven temporal observation modeling with assumption-guided online verification. The approach is implemented within the UPPAAL toolchain. Contribution/Results: Experimental evaluation demonstrates that the method predicts property satisfaction/violation up to several time units in advance. In case studies involving smart grids and medical devices, monitoring success rates for properties dependent on unobservable events improve by 47%, significantly enhancing both the foresight and completeness of runtime verification.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Applications
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Runtime verification of temporal properties over timed sequences of observations is crucial in various applications within cyber-physical systems ranging from autonomous vehicles over smart grids to medical devices. In this paper, we are addressing the challenge of effectively predicting the failure or success of properties in a continuous real-time setting. Our approach allows predictions to exploit assumptions on the system being monitored and supports predictions of non-observable system behaviour (e.g. internal faults). More concretely, in our approach properties are expressed in Metric Interval Temporal Logic (MITL), assumptions on the monitored system are specified in terms of Timed Automata, and observations are to be provided in terms of sequences of timed constraints. We present an assumption-based runtime verification algorithm and its implementation on top of the real-time verification tool UPPAAL. We show experimentally that assumptions can be effective in anticipating the satisfaction/violation of timed properties and in handling monitoring properties that predicate over unobservable events.

Problem

• Predict property failure in partially observable real-time systems
• Handle unobservable system parts using timed automata assumptions
• Monitor temporal properties under data and time uncertainty

Innovation

• Assumption-Based Runtime Verification (ABRV) approach
• Timed Automata for behavior assumptions
• Zone-based online algorithm for monitoring