This paper proposes a model to estimate the decision complexity and effort required to apply quantitative confidence assessment methods to assurance cases. The model considers both the worst and average case for these measures and characterizes how these quantities scale with argument size. Prior work has indicated that the additional effort required to apply these methods is a barrier to their adoption by assurance case practitioners. Researchers developing new methods, or improving existing methods, can use this model to estimate the effort required to apply their method. The proposed model is parameterized using data from published case studies and is applied to three existing quantitative confidence assessment methods: the Bayesian Belief Network method, the Dempster-Shafer Theory method, and the Certus method. The results show that, while Certus has the highest worst-case decision complexity, its average-case effort is lower than the BBN and DST methods.

翻译：本文提出一种模型，用于估算将定量置信度评估方法应用于保障案例所需的决策复杂度和工作量。该模型同时考虑最坏情况和平均情况下的度量指标，并刻画这些指标随论证规模变化的比例关系。先前研究表明，应用这些方法所需的额外工作量已成为保障案例从业者采纳它们的主要障碍。研究人员在开发新方法或改进现有方法时，可利用此模型预估其方法所需的工作量。本文基于已发表案例研究数据对该模型进行参数化，并将其应用于三种现有定量置信度评估方法：贝叶斯信念网络方法、邓普斯特-谢弗理论方法及Certus方法。结果表明，虽然Certus方法在最坏情况下的决策复杂度最高，但其平均工作量低于BBN和DST方法。