相关论文: Assurance via workflow+ modelling and conformance
The software development life cycle (SDLC) is a procedure used to develop a software system that meets both the customer s needs and real-world requirements. The first phase of the SDLC involves creating a conceptual model that represents…
Many industrial software development processes today have to comply with security standards such as the IEC~62443-4-1. These standards, written in natural language, are ambiguous and complex to understand. This is especially true for…
The adoption of large language models in safety-critical system engineering is constrained by trustworthiness, traceability, and alignment with established verification practices. We propose workflow-level design principles for trustworthy…
This paper presents an approach to model features and function nets of automotive systems comprehensively. In order to bridge the gap between feature requirements and function nets, we describe an approach to describe both using a…
The workflow concept, proliferated through the recently emergent computer supported cooperative work (CSCW) systems and workflow systems, advances information systems (IS) implementation models by incorporating aspects of collaboration and…
The Partially Ordered Workflow Language (POWL) has recently emerged as a process modeling notation, offering strong quality guarantees and high expressiveness. While early versions of POWL relied on strict block-structured operators for…
With an increasing degree of automation, automated vehicle systems become more complex in terms of functional components as well as interconnected hardware and software components. Thus, holistic systems engineering becomes a severe…
Scientific workflows have been used almost universally across scientific domains, and have underpinned some of the most significant discoveries of the past several decades. Many of these workflows have high computational, storage, and/or…
Diffusion models can learn rich representations during data generation, showing potential for Self-Supervised Learning (SSL), but they face a trade-off between generative quality and discriminative performance. Their iterative sampling also…
This paper describes the comprehensive safety framework that underpinned the development, release process, and regulatory approval of BMW's first SAE Level 3 Automated Driving System. The framework combines established qualitative and…
Learning enabled autonomous systems provide increased capabilities compared to traditional systems. However, the complexity of and probabilistic nature in the underlying methods enabling such capabilities present challenges for current…
Diffusion models have revolutionized generative tasks through high-fidelity outputs, yet flow matching (FM) offers faster inference and empirical performance gains. However, current foundation FM models are computationally prohibitive for…
Algorithmic verification of realistic systems to satisfy safety and other temporal requirements has suffered from poor scalability of the employed formal approaches. To design systems with rigorous guarantees, many approaches still rely on…
Even if the verification of authentication protocols can be achieved by means of formal analysis, the modelling of such an activity is an error-prone task due to the lack of automated and integrated processes. This paper proposes a…
The paper considers the complexity of verifying that a finite state system satisfies a number of definitions of information flow security. The systems model considered is one in which agents operate synchronously with awareness of the…
A widespread design approach in distributed applications based on the service-oriented paradigm, such as web-services, consists of clearly separating the enforcement of authorization policies and the workflow of the applications, so that…
Flow matching (FM) has shown promising results in data-driven planning. However, it inherently lacks formal guarantees for ensuring state and action constraints, whose satisfaction is a fundamental and crucial requirement for the safety and…
Existing ML benchmarks lack the depth and diversity of annotations needed for evaluating models on business process management (BPM) tasks. BPM is the practice of documenting, measuring, improving, and automating enterprise workflows.…
The rapid proliferation of large language models (LLMs) has intensified the requirement for reliable safety evaluation to uncover model vulnerabilities. To this end, numerous LLM safety evaluation benchmarks are proposed. However, existing…
This paper addresses the current lack of a unified formal framework in machine learning theory, as well as the absence of robust theoretical foundations for interpretability and ethical safety assurance. We first construct a formal…