Predictive Run-Time Verification of Discrete-Time Reachability Properties in Black-Box Systems Using Trace-Level Abstraction and Statistical Learning

Run-time Verification (RV) has become a crucial aspect of monitoring black-box systems. Recently, we introduced \(\mathcal {P}{} \textit{revent} \), a predictive RV framework, in which the monitor is able to predict the future extensions of an execution, using a model inferred from the random sample executions of the system. The monitor maintains a table of the states of the prediction model, with the probability of the extensions from each state that satisfy a safety property.

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