Formal Evidence Generation for Assurance Cases for Robotic Software Models

TL;DR

This paper introduces a model-based method that automates the generation of assurance case evidence for robotic software by integrating formal verification tools into the development workflow, improving safety assurance processes.

Contribution

It presents a systematic approach to derive formal assertions from natural language requirements and automate evidence generation using RoboChart, combining model checking and theorem proving.

Findings

Automated transformation of requirements into formal assertions.

Integration of multiple formal verification tools.

Successful application in case studies.

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.