A Set‐Based Planning Methodology to Plan and Execute Dynamic Verification Strategies Under Knowable Uncertainty

ABSTRACT In current practice, a verification strategy (VS) is planned by considering various uncertainties at the beginning of a development program. All relevant uncertainties are expected to be calibrated to facilitate the evaluation of the planned verification strategies. However, not all calibration information about uncertainties may be available at the beginning and some additional activities to discover unknown uncertainties may be necessary. This paper presents a set‐based planning (SBP) methodology for designing and executing system verification strategies under knowable uncertainty (KU). Building on a Bayesian network model of verification and correction activities (CAs), we introduce preparation activities that quantify KU, enabling dynamic planning as new information becomes available. Using a notional satellite communication instrument as a case study, we compare SBP against traditional static planning across scenarios with varying estimation errors in likelihood ratio (LR) values. Results show that SBP consistently achieves higher expected values, with substantial gains when CAs have low success rates. The findings provide practical guidance on when dynamic verification planning yields value and lay the groundwork for extensions to broader classes of uncertainty.