Building a safety case is a common approach to make expert judgement explicit about safety of a system. The issue of confidence in such argumentation is still an open research field. Providing quantitative estimation of confidence is an interesting approach to manage complexity of arguments. This paper explores the main current approaches, and proposes a new model for quantitative confidence estimation based on Belief Theory for its definition, and on Bayesian Belief Networks for its propagation in safety case networks.
In @cite_26 , the inventors of GSN address the confidence issue, by proposing to split a traditional safety case in two pieces. The first is the safety argument, showing all evidences, and the second is a confidence argument that addresses confidence in evidences, contexts, and individual inferences. This confidence argument is also represented with GSN. It starts by adding to the safety case some possible uncertainty sources, which are called Assurance Claim Points (ACP), that are attached to inferences (the arrows connecting claims), contexts (explanatory information), or solutions. Then, for each ACP, an argumentation mainly focuses on demonstrating that the ACP is trustworthy and appropriate, which is built using GSN. Another proposal @cite_7 , is based on the ACP but only focuses on Context and Solution elements. The authors propose to use a map (Common Characteristic Map) as a check list to identify sources of uncertainties, with recursive dependencies. For instance, if a safety case includes a solution which is a "Process result", they propose the generic uncertainties related to "the use of a language", "the use of a tool", "the use of a mechanism", "the involved artifacts", etc. All those characteristics are then refined, with possible recursive dependencies.
@cite_26 @cite_7
2170178247 2626217303 1579747287 2058033966
We present AdvoCATE, an Assurance Case Automation ToolsEt, to support the automated construction and assessment of safety cases. In addition to manual creation and editing, it has a growing suite of automated features. In this paper, we highlight its capabilities for (i) inclusion of specific metadata, (ii) translation to and from various formats, including those of other widely used safety case tools, (iii) composition, with auto-generated safety case fragments, and (iv) computation of safety case metrics which, we believe, will provide a transparent, quantitative basis for assessment of the state of a safety case as it evolves. The tool primarily supports the Goal Structuring Notation (GSN), is compliant with the GSN Community Standard Version 1, and the Object Modeling Group Argumentation Metamodel (OMG ARM). We present a novel approach to proving the absence of timing channels. The idea is to partition the programâ s execution traces in such a way that each partition component is checked for timing attack resilience by a time complexity analysis and that per-component resilience implies the resilience of the whole program. We construct a partition by splitting the program traces at secret-independent branches. This ensures that any pair of traces with the same public input has a component containing both traces. Crucially, the per-component checks can be normal safety properties expressed in terms of a single execution. Our approach is thus in contrast to prior approaches, such as self-composition, that aim to reason about multiple (kâ � 2) executions at once. We formalize the above as an approach called quotient partitioning, generalized to any k-safety property, and prove it to be sound. A key feature of our approach is a demand-driven partitioning strategy that uses a regex-like notion called trails to identify sets of execution traces, particularly those influenced by tainted (or secret) data. We have applied our technique in a prototype implementation tool called Blazer, based on WALA, PPL, and the brics automaton library. We have proved timing-channel freedom of (or synthesized an attack specification for) 24 programs written in Java bytecode, including 6 classic examples from the literature and 6 examples extracted from the DARPA STAC challenge problems. We introduce assured safety arguments, a new structure for arguing safety in which the safety argument is accompanied by a confidence argument that documents the confidence in the structure and bases of the safety argument. This structure separates the major components that have traditionally been confused within a single safety argument structure. Separation gives both arguments greater clarity of purpose, and helps avoid the introduction of superfluous arguments and evidence. In this paper we describe a systematic approach to establishing both arguments, illustrated with a running example. This paper presents a new approach for automatically deriving worst-case resource bounds for C programs. The described technique combines ideas from amortized analysis and abstract interpretation in a unified framework to address four challenges for state-of-the-art techniques: compositionality, user interaction, generation of proof certificates, and scalability. Compositionality is achieved by incorporating the potential method of amortized analysis. It enables the derivation of global whole-program bounds with local derivation rules by naturally tracking size changes of variables in sequenced loops and function calls. The resource consumption of functions is described abstractly and a function call can be analyzed without access to the function body. User interaction is supported with a new mechanism that clearly separates qualitative and quantitative verification. A user can guide the analysis to derive complex non-linear bounds by using auxiliary variables and assertions. The assertions are separately proved using established qualitative techniques such as abstract interpretation or Hoare logic. Proof certificates are automatically generated from the local derivation rules. A soundness proof of the derivation system with respect to a formal cost semantics guarantees the validity of the certificates. Scalability is attained by an efficient reduction of bound inference to a linear optimization problem that can be solved by off-the-shelf LP solvers. The analysis framework is implemented in the publicly-available tool C4B. An experimental evaluation demonstrates the advantages of the new technique with a comparison of C4B with existing tools on challenging micro benchmarks and the analysis of more than 2900 lines of C code from the cBench benchmark suite.