相关论文: Assurance via workflow+ modelling and conformance

Designing, assuring and releasing safe automated vehicles is a highly interdisciplinary process. As complex systems, automated driving systems will inevitably be subject to emergent properties, i. e., the properties of the overall system…

Modern machine learning workloads such as large language model training, fine-tuning jobs are highly distributed and span across hundreds of systems with multiple GPUs. Job completion time for these workloads is the artifact of the…

This paper examines the problem of achieving "grace" when controlling dynamical systems for safety, which is defined in terms of providing multi-layered safety assurances. Namely, two safety layers are created: a primary layer that…

OpenFlow is one of the most commonly used protocols for communication between the controller and the forwarding element in a software defined network (SDN). A model based on M/M/1 queues is proposed in [1] to capture the communication…

Machine Learning (ML) has already fundamentally changed several businesses. More recently, it has also been profoundly impacting the computational science and engineering domains, like geoscience, climate science, and health science. In…

Dependability assurance of systems embedding machine learning(ML) components---so called learning-enabled systems (LESs)---is a key step for their use in safety-critical applications. In emerging standardization and guidance efforts, there…

Two emerging architectural paradigms, i.e., Software Defined Networking (SDN) and Network Function Virtualization (NFV), enable the deployment and management of Service Function Chains (SFCs). A SFC is an ordered sequence of abstract…

Safety cases become increasingly important for software certification. Models play a crucial role in building and combining information for the safety case. This position paper sketches an ideal model-based safety case with defect…

As AI models scale to billions of parameters and operate with increasing autonomy, ensuring their safe, reliable operation demands engineering-grade security and assurance frameworks. This paper presents an enterprise-level, risk-aware,…

Nowadays, business enterprises often need to dynamically reconfigure their internal processes in order to improve the efficiency of the business flow. However, modifications of the workflow usually lead to several problems in terms of…

Machine Learning (ML) models are increasingly integrated into safety-critical systems, such as autonomous vehicle platooning, to enable real-time decision-making. However, their inherent imperfection introduces a new class of failure:…

Safety is one of the biggest concerns to applying reinforcement learning (RL) to the physical world. In its core part, it is challenging to ensure RL agents persistently satisfy a hard state constraint without white-box or black-box…

The aim of this paper is to propose a rigorous and complete design framework for complex system based on system engineering (SE) principles. The SE standard EIA-632 is used to guide the approach. Within this framework, two aspects are…

Cloud computing services provide scalable and cost-effective solutions for data storage, processing, and collaboration. With their growing popularity, concerns about security vulnerabilities are increasing. To address this, first, we…

In the field of computational science and engineering, workflows often entail the application of various software, for instance, for simulation or pre- and postprocessing. Typically, these components have to be combined in arbitrarily…

Model-based safety analysis approaches aim at finding critical failure combinations by analysis of models of the whole system (i.e. software, hardware, failure modes and environment). The advantage of these methods compared to traditional…

Workflows are prevalent in today's computing infrastructures. The workflow model support various different domains, from machine learning to finance and from astronomy to chemistry. Different Quality-of-Service (QoS) requirements and other…

The last decade has sparked several valiant efforts in deductive verification of distributed agreement protocols such as consensus and leader election. Oddly, there have been far fewer verification efforts that go beyond the core protocols…

Methods: This work introduces a method supporting the collaborative definition of machine learning tasks by leveraging model-based engineering in the formalization of the systems modeling language SysML. The method supports the…

Considering the ever-evolving threat landscape and rapid changes in software development, we propose a risk assessment framework called SAFER (Software Analysis Framework for Evaluating Risk). This framework is based on the necessity of a…