Formal Evidence Generation for Assurance Cases for Robotic Software Models
Formal Evidence Generation for Assurance Cases for Robotic Software Models
This research presents a model-based approach to systematically generate assurance case evidence for robotic software by embedding formal verification into the workflow. The method addresses challenges in deriving formal assertions from natural language requirements, orchestrating multiple verification tools, and integrating results. Using RoboChart, a domain-specific modelling language with formal semantics, the approach combines model checking and theorem proving to automatically transform structured requirements into formal assertions and integrate verification outcomes as evidence. Case studies demonstrate the effectiveness of this method.
The paper proposes a model-based workflow that embeds formal verification into assurance cases for robotic software, using RoboChart to automatically derive formal assertions from natural language requirements and integrate verification results as evidence.
Source: arXiv Robotics — research abstracts · Read original article ↗
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arXiv:2602.03550v2 Announce Type: replace-cross 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-bas
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Source：arXiv Robotics — research abstracts · arxiv.org