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Runtime monitoring plays a key role in the assurance of modern intelligentcyber-physical systems, which are frequently data-intensive andsafety-critical. While graph queries can serve as an expressive yet formallyprecise specification language to capture the safety properties of interest,there are no timeliness guarantees for such auto-generated runtime monitoringprograms, which prevents their use in a real-time setting. While worst-caseexecution time (WCET) bounds derived by existing static WCET estimationtechniques are safe, they may not be tight as they are unable to exploitdomain-specific (semantic) information about the input models. This paperpresents a semantic-aware WCET analysis method for data-driven monitoringprograms derived from graph queries. The method incorporates results obtainedfrom low-level timing analysis into the objective function of a modern graphsolver. This allows the systematic generation of input graph models up to aspecified size (referred to as witness models) for which the monitor isexpected to take the most time to complete. Hence the estimated execution timeof the monitors on these graphs can be considered as safe and tight WCET.Additionally, we perform a set of experiments with query-based programs runningon a real-time platform over a set of generated models to investigate therelationship between execution times and their estimates, and compare WCETestimates produced by our approach with results from two well-known timinganalyzers, aiT and OTAWA.

Worst-Case Execution Time Calculation for Query-Based Monitors by Witness Generation