Assessing Confidence in Assurance 2.0
Also issued as a report of the Clarissa project under the title "Assessing Confidence in Assurance Cases with Clarissa"

Robin Bloomfield (Adelard LLP and City, University of London) and John Rushby (SRI CSL)

Tech Report SRI-CSL-2022-02 and also available as arXiv 2205.04522

 *NEW* An overview of my papers on assurance cases


An assurance case is intended to provide justifiable confidence in the truth of its top claim, which typically concerns safety or security. A natural question is then "how much" confidence does the case provide?

In this report, we explore issues in assessing confidence for assurance cases developed using the rigorous approach we call Assurance 2.0. We argue that confidence cannot be reduced to a single attribute or measurement. Instead, we suggest it should be based on attributes that draw on three different perspectives: positive, negative, and residual doubts.

Positive Perspectives consider the extent to which the evidence and overall argument of the case combine to make a positive statement justifying belief in its claims. We set a high bar for justification, requiring it to be indefeasible. The primary positive measure for this is soundness, which interprets the argument as a logical proof and delivers a yes/no measurement. The interior steps of an Assurance 2.0 case can be evaluated as logical axioms, but the evidential steps at the leaves derive logical claims epistemically---from observations or measurements about the system and its environment---and must be treated as premises. Confidence in these can be expressed probabilistically and we use confirmation measures to ensure that the probabilistic "weight" of evidence crosses some threshold.

In addition, probabilities can be aggregated from evidence through the steps of the argument using probability logics to yield what we call probabilistic valuations for the claims (in contrast to soundness, which is a logical valuation). The aggregated probability attached to the top claim can be interpreted as a numerical measure of confidence. We apply probabilistic valuations only to sound cases, and this avoids some of the difficulties that attend probabilistic methods that stand alone. The primary uses for probabilistic valuations are with less critical systems, where we trade assurance effort against confidence, and in assessing residual doubts.

Negative Perspectives record doubts and challenges to the case, typically expressed as defeaters, and their exploration and resolution. Assurance developers must guard against confirmation bias and should vigorously explore potential defeaters as they develop the case, and should record them and their resolution to avoid rework and to aid reviewers.

Residual Doubts: the world is uncertain so not all potential defeaters can be resolved. For example, we may design a system to tolerate two faults and have good reasons and evidence to suppose that is sufficient to cover the exposure on any expected mission. But doubts remain: what if more than two faults do arrive? Here we can explore consequences and likelihoods and thereby assess risk (their product). Some of these residual risks may be unacceptable and thereby prompt a review, but others may be considered acceptable or unavoidable. It is crucial however that these judgments are conscious ones and that they are recorded in the assurance case.

This report examines each of these three perspectives in detail and indicates how Clarissa, our prototype toolset for Assurance 2.0, assists in their evaluation.




BibTeX Entry

	AUTHOR = {Robin Bloomfield and John Rushby},
	TITLE = {Confidence in {Assurance 2.0}},
	MONTH = may,
	YEAR = 2022,
	JOURNAL = {{\tt arXiv:2205.04522}}

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