# Towards Continuous Assurance with Formal Verification and Assurance Cases

arXiv: https://arxiv.org/abs/2511.14805 · HTML: https://mlsanity.com/paper/2511.14805/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 30 references that are in the map.
- First posted on arXiv 17 Nov 2025.

## Builds on (most cited first, 4 of 4)

- [Reliability Assessment and Safety Arguments for Machine Learning Components in System Assurance](https://mlsanity.com/paper/2112.00646/) (2021)
- [ReSonAte: A Runtime Risk Assessment Framework for Autonomous Systems](https://mlsanity.com/paper/2102.09419/) (2021)
- [On Specifying for Trustworthiness](https://mlsanity.com/paper/2206.11421/) (2022)
- [Towards Developing Safety Assurance Cases for Learning-Enabled Medical Cyber-Physical Systems](https://mlsanity.com/paper/2211.15413/) (2022)

## Abstract (arXiv)

Autonomous systems must sustain justified confidence in their correctness and safety across their operational lifecycle-from design and deployment through post-deployment evolution. Traditional assurance methods often separate development-time assurance from runtime assurance, yielding fragmented arguments that cannot adapt to runtime changes or system updates - a significant challenge for assured autonomy. Towards addressing this, we propose a unified Continuous Assurance Framework that integrates design-time, runtime, and evolution-time assurance within a traceable, model-driven workflow as a step towards assured autonomy. In this paper, we specifically instantiate the design-time phase of the framework using two formal verification methods: RoboChart for functional correctness and PRISM for probabilistic risk analysis. We also propose a model-driven transformation pipeline, implemented as an Eclipse plugin, that automatically regenerates structured assurance arguments whenever formal specifications or their verification results change, thereby ensuring traceability. We demonstrate our approach on a nuclear inspection robot scenario, and discuss its alignment with the Trilateral AI Principles, reflecting regulator-endorsed best practices.