A Scalability Analysis of Quantitative Confidence Assessment Methods for Assurance Cases

2026-06-16 · Source: cs.SE updates on arXiv.org · Field: Technology & Digital — Software Development & Engineering, Artificial Intelligence & Machine Learning · Depth: Expert, extended

Summary

A new numerical model quantifies the decision complexity and effort required to apply quantitative confidence assessment methods (CAMs) to assurance cases (ACs). This model, which considers both worst-case and average-case scenarios, characterizes how these measures scale with argument size. Parameterized using data from published AC case studies, the model was applied to three existing CAMs: the Bayesian Belief Network (BBN) method, the Dempster-Shafer Theory (DST) method, and the Certus method. The analysis revealed that Certus, while exhibiting the highest worst-case decision complexity (over 10,000 decisions for an argument with 350 nodes), demonstrates the lowest average-case effort. For an argument of approximately 350 nodes, the model predicts Certus requires 13 hours of effort on average, compared to 14 hours for BBN and 26 hours for DST. This work addresses a key barrier to CAM adoption identified by practitioners: the additional effort required.

Key takeaway

For researchers developing new quantitative confidence assessment methods, you should integrate scalability analysis early in your design process. Your method's average-case decision complexity and effort are critical for practitioner adoption, even if worst-case scenarios appear high. Calibrate your model parameters with empirical data to provide accurate effort predictions, ensuring your method is practical for real-world assurance cases.

Key insights

A numerical model can estimate the decision complexity and effort of quantitative confidence assessment methods for assurance cases.

Principles

• Effort is a key barrier to CAM adoption.
• Scalability varies significantly across CAMs.
• Average-case effort can differ from worst-case.

Method

A numerical model estimates decision complexity and effort for CAMs by modeling ACs as n-ary trees, quantifying user decisions for propagation and leaf valuations in worst and average cases, then translating to time.

In practice

• Use the model to evaluate new CAMs.
• Prioritize average-case effort for adoption.
• Consider Certus for lower average effort.

Topics

• Assurance Cases
• Confidence Assessment Methods
• Scalability Analysis
• Bayesian Belief Networks
• Dempster-Shafer Theory
• Certus Method

Best for: AI Scientist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by cs.SE updates on arXiv.org.