# A Methodology for Automating Assurance Case Generation

arXiv: https://arxiv.org/abs/2003.05388 · HTML: https://mlsanity.com/paper/2003.05388/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Mar 2020.

## Built on by (most cited first, 2 of 2)

- [GPT-4 and Safety Case Generation: An Exploratory Analysis](https://mlsanity.com/paper/2312.05696/) (2023)
- [Towards Continuous Assurance Case Creation for ADS with the Evidential Tool Bus](https://mlsanity.com/paper/2403.01918/) (2024)

## Abstract (arXiv)

Safety Case has become an integral component for safety-certification in various Cyber Physical System domains including automotive, aviation, medical devices, and military. The certification processes for these systems are stringent and require robust safety assurance arguments and substantial evidence backing. Despite the strict requirements, current practices still rely on manual methods that are brittle, do not have a systematic approach or thorough consideration of sound arguments. In addition, stringent certification requirements and ever-increasing system complexity make ad-hoc, manual assurance case generation (ACG) inefficient, time consuming, and expensive. To improve the current state of practice, we introduce a structured ACG tool which uses system design artifacts, accumulated evidence, and developer expertise to construct a safety case and evaluate it in an automated manner. We also illustrate the applicability of the ACG tool on a remote-control car testbed case study.