5 research outputs found

    Formal Assurance Arguments: A Solution In Search of a Problem?

    Get PDF
    An assurance case comprises evidence and argument showing how that evidence supports assurance claims (e.g., about safety or security). It is unsurprising that some computer scientists have proposed formalizing assurance arguments: most associate formality with rigor. But while engineers can sometimes prove that source code refines a formal specification, it is not clear that formalization will improve assurance arguments or that this benefit is worth its cost. For example, formalization might reduce the benefits of argumentation by limiting the audience to people who can read formal logic. In this paper, we present (1) a systematic survey of the literature surrounding formal assurance arguments, (2) an analysis of errors that formalism can help to eliminate, (3) a discussion of existing evidence, and (4) suggestions for experimental work to definitively answer the question

    The Simple Assurance Argument Interchange Format (SAAIF) Manual

    Get PDF
    This document describes the Simple Assurance Argument Interchange Format, a proposed meta-model for describing structured assurance arguments. We describe the syntax and semantics of the model elements, compare the meta-model to existing argument formats, and give an example to illustrate its use

    Towards a Clearer Understanding of Context and Its Role in Assurance Argument Confidence

    No full text

    An Investigation of Proposed Techniques for Quantifying Confidence in Assurance Arguments

    Get PDF
    The use of safety cases in certification raises the question of assurance argument sufficiency and the issue of confidence (or uncertainty) in the argument's claims. Some researchers propose to model confidence quantitatively and to calculate confidence in argument conclusions. We know of little evidence to suggest that any proposed technique would deliver trustworthy results when implemented by system safety practitioners. Proponents do not usually assess the efficacy of their techniques through controlled experiment or historical study. Instead, they present an illustrative example where the calculation delivers a plausible result. In this paper, we review current proposals, claims made about them, and evidence advanced in favor of them. We then show that proposed techniques can deliver implausible results in some cases. We conclude that quantitative confidence techniques require further validation before they should be recommended as part of the basis for deciding whether an assurance argument justifies fielding a critical system

    Understanding and Evaluating Assurance Cases

    Get PDF
    Assurance cases are a method for providing assurance for a system by giving an argument to justify a claim about the system, based on evidence about its design, development, and tested behavior. In comparison with assurance based on guidelines or standards (which essentially specify only the evidence to be produced), the chief novelty in assurance cases is provision of an explicit argument. In principle, this can allow assurance cases to be more finely tuned to the specific circumstances of the system, and more agile than guidelines in adapting to new techniques and applications. The first part of this report (Sections 1-4) provides an introduction to assurance cases. Although this material should be accessible to all those with an interest in these topics, the examples focus on software for airborne systems, traditionally assured using the DO-178C guidelines and its predecessors. A brief survey of some existing assurance cases is provided in Section 5. The second part (Section 6) considers the criteria, methods, and tools that may be used to evaluate whether an assurance case provides sufficient confidence that a particular system or service is fit for its intended use. An assurance case cannot provide unequivocal "proof" for its claim, so much of the discussion focuses on the interpretation of such less-than-definitive arguments, and on methods to counteract confirmation bias and other fallibilities in human reasoning
    corecore