Disruptive Innovations and Disruptive Assurance: Assuring Machine Learning and Autonomy

Autonomous and machine learning-based systems are disruptive innovations and thus require a corresponding disruptive assurance strategy. We offer an overview of a framework based on claims, arguments, and evidence aimed at addressing these systems and use it to identify specific gaps, challenges, and potential solutions

Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 1

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. Part 2: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines

Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS - a collection of Technical Notes Part 2

This report provides an introduction and overview of the Technical Topic Notes (TTNs) produced in the Towards Identifying and closing Gaps in Assurance of autonomous Road vehicleS (Tigars) project. These notes aim to support the development and evaluation of autonomous vehicles. Part 1 addresses: Assurance-overview and issues, Resilience and Safety Requirements, Open Systems Perspective and Formal Verification and Static Analysis of ML Systems. This report is Part 2 and discusses: Simulation and Dynamic Testing, Defence in Depth and Diversity, Security-Informed Safety Analysis, Standards and Guidelines

Fairness for Infinite-State Systems

In this paper we introduce the first known tool for symbolically proving fair-CTL properties of (infinite-state) integer programs. Our solution is based on a reduction to existing techniques for fairness- free CTL model checking via the use of infinite non-deterministic branching to symbolically partition fair from unfair executions. We show the viability of our approach in practice using examples drawn from device drivers and algorithms utilizing shared resources

On Automation of CTL* Verification for Infinite-State Systems

In this paper we introduce the first known fully automated tool for symbolically proving CTL* properties of (infinite-state) integer programs. The method uses an internal encoding which facilitates reasoning about the subtle interplay between the nesting of path and state temporal operators that occurs within \ctlstar proofs. A precondition synthesis strategy is then used over a program transformation which trades nondeterminism in the transition relation for nondeterminism explicit in variables predicting future outcomes when necessary. We show the viability of our approach in practice using examples drawn from device drivers and various industrial examples