A product to be certified follows a design, implementation, verification and validation cycle. At the beginning of the cycle, the product owner only relies, for the verification and validation aspects, on an Assurance Case (AC) template that provides choices in a tree structure. The difficulty for making decisions among choices is high when the product is based on a new technology with a large number of approaches with different levels of readiness, as it is the case for robust Machine Learning (ML). In those cases an uncertainty assessment can be useful for making a judgment about the opportunity of using a specific approach. Based on recently published results on uncertainty elicitation and propagation in Goal Structuring Notation models of AC, the work presented here justify and implements an uncertainty assessment based simultaneously on qualitative and quantitative uncertainty modeling. Moreover, it proposes an elicitation method allowing simultaneous capture of qualitative and quantitative uncertainty and an analysis of uncertainty modeling and propagation on AC templates. Finally, it demonstrates the approach with an use case related to robustness of ML models. The result of this research will be integrated in the Capella system engineering environment.