Building on this, Assurance 2.0 is an approach to the development and presentation of assurance cases that is intended to make their construction and assessment more straightforward, yet also more rigorous. In fact, it is rigor that enables straightforwardness because it reduces the "bewilderment of choice" and makes assurance cases more systematic and predictable. Assurance 2.0 employs several ideas that are not in themselves new, but integrates them in a way that we believe is coherent and effective.
Our papers on Assurance 2.0 are listed below in reverse chronological order. We suggest starting with either the "Manifesto" paper (broad but light on details) or the one from Cliff Jones' Festschrift (more technical but also more narrowly focused). Look at the 2-page "Nutshell" when you need a really high-level overview or memory aid.
Clarissa supports the construction and evaluation of assurance cases using Assurance 2.0, and provides tools for logical and probabilistic assessment, defeaters, and residual doubts. It also has a synthesis assistant that can synthesize assurance (sub)cases from templates provided in a theory.
In addition Clarissa has tools for exploring semantic properties. It can use an LLM to translate claims into a logical representation, then perform reasoning using Answer Set Programming with s(CASP) to examine properties such as consistency, certain forms of well-formedness, and completeness.
Return to my bibliography page.
John Rushby (R u s h b y @ c s l . s r i . c o m)