Formal Evidence Generation for Assurance Cases for Robotic Software Models

2026-02-03

AI Summary

This work addresses the challenge of providing verifiable safety assurance for robotic systems in safety-critical domains, where traditional assurance cases rely on manually generated evidence that is costly, error-prone, and difficult to maintain. The paper proposes a model-based automated approach that deeply integrates formal verification into the assurance workflow. It employs RoboChart—a domain-specific modeling language with formal semantics—to capture system designs, and introduces a template-driven mechanism to automatically translate natural-language requirements into formal assertions. These assertions are then discharged through a combination of model checking and theorem proving tools, yielding formally verified evidence that can be seamlessly integrated into assurance cases. Case studies demonstrate that the proposed method significantly enhances the reliability, maintainability, and degree of automation in safety argumentation.

Technology Category

• Natural Language Processing: Safety and Robustness
• Knowledge Representation and Reasoning: Automated Reasoning and Theorem Proving
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization

Abstract

Robotics and Autonomous Systems are increasingly deployed in safety-critical domains, so that demonstrating their safety is essential. Assurance Cases (ACs) provide structured arguments supported by evidence, but generating and maintaining this evidence is labour-intensive, error-prone, and difficult to keep consistent as systems evolve. We present a model-based approach to systematically generating AC evidence by embedding formal verification into the assurance workflow. The approach addresses three challenges: systematically deriving formal assertions from natural language requirements using templates, orchestrating multiple formal verification tools to handle diverse property types, and integrating formal evidence production into the workflow. Leveraging RoboChart, a domain-specific modelling language with formal semantics, we combine model checking and theorem proving in our approach. Structured requirements are automatically transformed into formal assertions using predefined templates, and verification results are automatically integrated as evidence. Case studies demonstrate the effectiveness of our approach.

Problem

• Assurance Cases
• Robotic Software
• Formal Evidence
• Safety Assurance
• Verification

Innovation

• formal verification
• assurance cases
• RoboChart
• model-based evidence generation
• automated assertion derivation