Evidence management for evolutionary safety assurance and certification

Abstract

Safety assurance and certification are amongst the most expensive and time-consuming activities in the development of safety-critical systems. Deeming a system to be safe involves gathering convincing evidence to argue the safe operation of the system, usually according to the requirements of some safety standard. To handle large collections of safety evidence effectively, practitioners need knowledge of how to classify different types of evidence, how to structure the evidence to show fulfilment of standards' requirements, and how to assess the evidence. However, the notion of evidence is vague and safety standards' requirements can be ambiguous and difficult to understand. Major problems also arise when a system evolves, as the body of safety evidence has to be adequately maintained in order to ensure system safety and allow its demonstration. In this context, this PhD aims to propose a framework for safety evidence management in evolutionary scenarios. The thesis work will concentrate on devising a model-based and customizable infrastructure for storage, manipulation, reuse, and analysis of evolving safety evidence. The infrastructure will be developed and evaluated in the scope of OPENCOSS a large-scale European research project.