Systematic Evaluation of (Safety) Assurance Cases

Chowdhury, Thomas; Wassyng, Alan; Paige, Richard F.; Lawford, Mark

doi:10.1007/978-3-030-54549-9_2

Cited by 7 publications

(2 citation statements)

References 4 publications

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“…The following coffee cup example originated in McSCert, with an early version of the top-level of the AC published in 2020 [3]. We explored ACs that did not include software since we wanted to focus on safety (freedom from loss/harm), irrespective of the nature of the system.…”

Section: A Fast Food Coffee Cup Examplementioning

confidence: 99%

Using Assurance Cases to Prevent Malicious Behaviour from Targeting Safety Vulnerabilities

Bandur,

Lawford,

Mosser

et al. 2023

Lecture Notes in Computer Science

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Section: A Fast Food Coffee Cup Examplementioning

confidence: 99%

Using Assurance Cases to Prevent Malicious Behaviour from Targeting Safety Vulnerabilities

Bandur,

Lawford,

Mosser

et al. 2023

Lecture Notes in Computer Science

Self Cite

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“…Observe that the requirements for soundness (indefeasibility, deductiveness, confirmation measures), although rigorous, are straightforward: there is no doubt what is required in both construction and evaluation. This is what we mean by "simplicity through rigor" and it may be contrasted with the complex criteria proposed for less constrained forms of assurance case [28].…”

Section: Structure Of An Assurance 20 Casementioning

confidence: 99%

Assessing Confidence with Assurance 2.0

Rushby¹,

Bloomfield²

2022

Preprint

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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 ...

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Overcome Certification Challenges of AI Based Airborne Systems Using FAA Overarching Properties

Paramasivam,

Paulraj

et al. 2023

INCOSE International Symp

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Technologies such as Artificial Intelligence encompassing Machine Learning, Deep Learning techniques have proved to be a game changer in multiple sectors. The use of these technologies in Aviation will prove to ensure higher rigor in predictive maintainability, air traffic management, safety assurance and airworthiness. Current traditional regulatory and industry standards have proved to be efficient and effective in providing highest level of safety. However, Certification of Avionics systems is becoming challenging because of increasing complexity of avionics systems. Current regulations and standards are very prescriptive and inadequate to support new technological trends. Civil Aviation Authorities (CAAs) are working on establishing the regulatory guidance around these technologies. This paper addresses the new strategy of employing Federal Aviation Administration (FAA) “Overarching Properties” ‐ a framework for demonstrating that a product has the qualities required to perform effectively and safely with the purpose of simplifying the certification process. This paper also covers the possible scenarios for establishing the safety net concept to enhance the determinism and airworthiness of AI based systems.

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Systematic Evaluation of (Safety) Assurance Cases

Cited by 7 publications

References 4 publications

Using Assurance Cases to Prevent Malicious Behaviour from Targeting Safety Vulnerabilities

Using Assurance Cases to Prevent Malicious Behaviour from Targeting Safety Vulnerabilities

Assessing Confidence with Assurance 2.0

Overcome Certification Challenges of AI Based Airborne Systems Using FAA Overarching Properties

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