ACCESS: Assurance Case Centric Engineering of Safety-critical Systems

• URL: http://arxiv.org/abs/2403.15236v2
• Date: Tue, 16 Apr 2024 10:55:37 GMT
• Title: ACCESS: Assurance Case Centric Engineering of Safety-critical Systems
• Authors: Ran Wei, Simon Foster, Haitao Mei, Fang Yan, Ruizhe Yang, Ibrahim Habli, Colin O'Halloran, Nick Tudor, Tim Kelly, Yakoub Nemouchi,
• Abstract summary: Assurance cases are used to communicate and assess confidence in critical system properties such as safety and security. In recent years, model-based system assurance approaches have gained popularity to improve the efficiency and quality of system assurance activities. We show how model-based system assurance cases can trace to heterogeneous engineering artifacts.
• Score: 9.388301205192082
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Assurance cases are used to communicate and assess confidence in critical system properties such as safety and security. Historically, assurance cases have been manually created documents, which are evaluated by system stakeholders through lengthy and complicated processes. In recent years, model-based system assurance approaches have gained popularity to improve the efficiency and quality of system assurance activities. This becomes increasingly important, as systems becomes more complex, it is a challenge to manage their development life-cycles, including coordination of development, verification and validation activities, and change impact analysis in inter-connected system assurance artifacts. Moreover, there is a need for assurance cases that support evolution during the operational life of the system, to enable continuous assurance in the face of an uncertain environment, as Robotics and Autonomous Systems (RAS) are adopted into society. In this paper, we contribute ACCESS - Assurance Case Centric Engineering of Safety-critical Systems, an engineering methodology, together with its tool support, for the development of safety critical systems around evolving model-based assurance cases. We show how model-based system assurance cases can trace to heterogeneous engineering artifacts (e.g. system architectural models, system safety analysis, system behaviour models, etc.), and how formal methods can be integrated during the development process. We demonstrate how assurance cases can be automatically evaluated both at development and runtime. We apply our approach to a case study based on an Autonomous Underwater Vehicle (AUV).

Related papers

• Automating Semantic Analysis of System Assurance Cases using Goal-directed ASP [1.2189422792863451]
  We present our approach to enhancing Assurance 2.0 with semantic rule-based analysis capabilities. We examine the unique semantic aspects of assurance cases, such as logical consistency, adequacy, indefeasibility, etc.
  arXiv  Detail & Related papers  (2024-08-21T15:22:43Z)
• EAIRiskBench: Towards Evaluating Physical Risk Awareness for Task Planning of Foundation Model-based Embodied AI Agents [47.69642609574771]
  Embodied artificial intelligence (EAI) integrates advanced AI models into physical entities for real-world interaction. Foundation models as the "brain" of EAI agents for high-level task planning have shown promising results. However, the deployment of these agents in physical environments presents significant safety challenges. This study introduces EAIRiskBench, a novel framework for automated physical risk assessment in EAI scenarios.
  arXiv  Detail & Related papers  (2024-08-08T13:19:37Z)
• A PRISMA-Driven Bibliometric Analysis of the Scientific Literature on Assurance Case Patterns [7.930875992631788]
  Assurance cases can be used to prevent system failure. They are structured arguments that allow arguing and relaying various safety-critical systems' requirements.
  arXiv  Detail & Related papers  (2024-07-06T05:00:49Z)
• Towards Guaranteed Safe AI: A Framework for Ensuring Robust and Reliable AI Systems [88.80306881112313]
  We will introduce and define a family of approaches to AI safety, which we will refer to as guaranteed safe (GS) AI. The core feature of these approaches is that they aim to produce AI systems which are equipped with high-assurance quantitative safety guarantees. We outline a number of approaches for creating each of these three core components, describe the main technical challenges, and suggest a number of potential solutions to them.
  arXiv  Detail & Related papers  (2024-05-10T17:38:32Z)
• Towards Continuous Assurance Case Creation for ADS with the Evidential Tool Bus [0.4194295877935868]
  An assurance case has become an integral component for the certification of safety-critical systems. We report on our preliminary experience leveraging the tool integration framework Evidential Tool Bus (ETB) for the construction and continuous maintenance of an assurance case.
  arXiv  Detail & Related papers  (2024-03-04T10:32:48Z)
• Leveraging Traceability to Integrate Safety Analysis Artifacts into the Software Development Process [51.42800587382228]
  Safety assurance cases (SACs) can be challenging to maintain during system evolution. We propose a solution that leverages software traceability to connect relevant system artifacts to safety analysis models. We elicit design rationales for system changes to help safety stakeholders analyze the impact of system changes on safety.
  arXiv  Detail & Related papers  (2023-07-14T16:03:27Z)
• Recursively Feasible Probabilistic Safe Online Learning with Control Barrier Functions [60.26921219698514]
  We introduce a model-uncertainty-aware reformulation of CBF-based safety-critical controllers. We then present the pointwise feasibility conditions of the resulting safety controller. We use these conditions to devise an event-triggered online data collection strategy.
  arXiv  Detail & Related papers  (2022-08-23T05:02:09Z)
• Joint Differentiable Optimization and Verification for Certified Reinforcement Learning [91.93635157885055]
  In model-based reinforcement learning for safety-critical control systems, it is important to formally certify system properties. We propose a framework that jointly conducts reinforcement learning and formal verification.
  arXiv  Detail & Related papers  (2022-01-28T16:53:56Z)
• Dos and Don'ts of Machine Learning in Computer Security [74.1816306998445]
  Despite great potential, machine learning in security is prone to subtle pitfalls that undermine its performance. We identify common pitfalls in the design, implementation, and evaluation of learning-based security systems. We propose actionable recommendations to support researchers in avoiding or mitigating the pitfalls where possible.
  arXiv  Detail & Related papers  (2020-10-19T13:09:31Z)
• Quantifying Assurance in Learning-enabled Systems [3.0938904602244355]
  Dependability assurance of systems embedding machine learning components is a key step for their use in safety-critical applications. This paper develops a quantitative notion of assurance that an LES is dependable, as a core component of its assurance case. We illustrate the utility of assurance measures by application to a real world autonomous aviation system.
  arXiv  Detail & Related papers  (2020-06-18T08:11:50Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.