Formal Evidence Generation for Assurance Cases for Robotic Software Models
Overview
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.
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- arXiv Robotics — research abstractsSignalFormal 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.
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