Worst-case timing analysis traditionally begins with estimating the worst-case execution time (WCET) of individual tasks using either static analysis or measurement-based techniques. To derive worst-case response times (WCRTs), engineers typically compose these WCETs with bounds on preemption and operating system overheads.

However, WCRTs depend on complex system-level interactions, including task communication, OS behavior, and asynchronous events. Compositional analysis often overestimates, assuming that worst-case conditions across components coincide, admitting infeasible global control-flow paths. Static whole-system techniques refine this by modeling the system holistically but require platform-specific tailoring or extensive annotations. A dynamic equivalent has been missing.

We present FRET, the first dynamic whole-system approach for estimating WCRTs. FRET employs feedback-guided fuzzing to uncover timing-critical dependencies, including inter-task communication, task/OS interactions, and interrupt effects, without requiring prior knowledge of inputs or states. Implemented using LibAFL and evaluated on FreeRTOS with realistic benchmarks, FRET consistently outperforms state-of-the-art fuzzing strategies in estimating accurate response times. While not sound, FRET offers actionable insights that complement traditional analyses and support system validation, runtime monitoring, and robust mixed-criticality scheduling.