Assurance Case Centric Engineering of Safety-critical Systems

Основные понятия

Developing safety-critical systems with an assurance case-centric approach.

Аннотация

The content discusses the development of safety-critical systems using an assurance case-centric engineering methodology called ACCESS. It emphasizes the importance of model-based system assurance and highlights the challenges in managing complex system development life cycles. The paper introduces a tool, ACME, to support the creation and management of assurance cases based on the Structured Assurance Case Metamodel (SACM). The methodology is applied to a case study involving an Autonomous Underwater Vehicle (AUV), showcasing how model-based assurance cases can be traced to engineering artifacts for automated evaluation.
Structure:

Introduction to Assurance Cases
Model-Based System Assurance Approaches
Structured Assurance Case Metamodel (SACM)
Application of ACCESS Methodology
Tool Support with ACME
Case Study: AUV Development Process

Статистика

"Assurance cases are used to communicate and assess confidence in critical system properties such as safety and security."
"Model-based system assurance approaches have gained popularity to improve efficiency and quality."
"Historically, assurance cases have been manually created documents evaluated by stakeholders."

Цитаты

"Assurance cases provide an explicit means for arguing, justifying, and assessing confidence in the safety of safety-critical systems."
"Existing model-based assurance case approaches cannot provide collective and automated evaluation of an assurance case."

Ключевые выводы из

ACCESS

by Ran Wei,Simo... в arxiv.org 03-25-2024

https://arxiv.org/pdf/2403.15236.pdf

Дополнительные вопросы

How can automation improve the efficiency of managing complex system development life cycles?

Automation can significantly enhance the efficiency of managing complex system development life cycles in several ways:

Automated Traceability: Automation tools can establish and maintain traceability between different engineering artifacts, ensuring that changes in one artifact are reflected in others. This reduces manual effort and minimizes errors.
Automated Validation and Verification: By automating validation and verification processes, such as checking requirements against models or performing formal analysis on design artifacts, errors can be detected early on, saving time and resources.
Automated Change Management: Automation tools can track changes in engineering artifacts and automatically update related components, reducing the risk of inconsistencies during development.
Efficient Documentation: Automated tools can generate documentation based on the latest version of engineering artifacts, ensuring that all stakeholders have access to up-to-date information without manual intervention.

What are the potential drawbacks or limitations of relying on model-based system assurance approaches?

While model-based system assurance approaches offer numerous benefits, there are some potential drawbacks to consider:

Complexity: Developing detailed models for assurance purposes may require specialized skills and expertise, making it challenging for teams without experience in modeling techniques.
Model Maintenance: Models need to be updated regularly to reflect changes in requirements or design decisions. Failure to maintain models accurately could lead to discrepancies between the model representation and the actual system behavior.
Tool Dependency: Relying heavily on automated tools for modeling and analysis means that teams may become overly dependent on specific software solutions, limiting flexibility.
Validation Challenges: Ensuring that models accurately represent real-world systems requires thorough validation efforts which could be time-consuming.

How can advancements in Robotics and Autonomous Systems impact traditional safety assurance practices?

Advancements in Robotics and Autonomous Systems (RAS) introduce new challenges to traditional safety assurance practices:

Dynamic Environments: RAS operate in dynamic environments where conditions change rapidly; this necessitates continuous monitoring at runtime rather than relying solely on pre-defined safety cases developed during design phase.
Adaptive Behavior: The adaptive nature of RAS introduces uncertainties into their behavior; traditional static safety cases may not adequately address these uncertainties requiring a shift towards dynamic safety case management strategies.
Interconnected Systems : As RAS often interact with other systems or devices (Internet-of-Things), traditional siloed safety assurances might not suffice; integrated approach considering broader ecosystem interactions is essential
4 .Continuous Assurance: With evolving technologies like AI/ML impacting RAS capabilities over time , continuous reassessment through automated methods becomes crucial for maintaining operational integrity while adapting regulatory compliance standards .