A Scalable Framework for the Management of STPA Requirements: a Case Study on eVTOL Operations

• URL: http://arxiv.org/abs/2508.16708v1
• Date: Fri, 22 Aug 2025 13:26:00 GMT
• Title: A Scalable Framework for the Management of STPA Requirements: a Case Study on eVTOL Operations
• Authors: Shufeng Chen, Halima El Badaoui, Mariat James Elizebeth, Takuya Nakashima, Siddartha Khastgir, Paul Jennings,
• Abstract summary: System-Theoretic Process Analysis (STPA) is a recommended method for analysing complex systems.<n>The absence of a structured framework for managing and prioritising requirements presents challenges in fast-paced development environments.<n>This paper introduces a scalable framework for prioritising aSTPA-derived requirements.
• Score: 1.3898092652070853
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: System-Theoretic Process Analysis (STPA) is a recommended method for analysing complex systems, capable of identifying thousands of safety requirements often missed by traditional techniques such as Failure Mode and Effects Analysis (FMEA) and Fault Tree Analysis (FTA). However, the absence of a structured framework for managing and prioritising these requirements presents challenges, particularly in fast-paced development environments. This paper introduces a scalable framework for prioritising STPA-derived requirements. The framework integrates outputs from each STPA step and incorporates expert evaluations based on four key factors: implementation time, cost, requirement type, and regulatory coverage. To reduce subjectivity, Monte-Carlo Simulation (MCS) is employed to calculate and stabilise requirement rankings. An automation toolchain supports the framework, enabling dynamic mapping of prioritised requirements in a scaling matrix. This visualisation aids decision-making and ensures traceability across development phases. The framework is applicable from early conceptualisation to more advanced stages, enhancing its utility in iterative system development. The framework was validated through a real-world case study focused on Electric Vertical Take-off and Landing (eVTOL) operations, conducted in collaboration with the UK Civil Aviation Authority. The findings contributed directly to CAP3141, a Civil Aviation Publication that identifies systemic operational risks and safety mitigations for regulators, operators, and vertiports. The prioritisation process supported decision-making by helping stakeholders identify and manage high-impact requirements efficiently. This work contributes a practical solution for managing STPA outputs, bridging gaps in requirement prioritisation and supporting safety-critical development in emerging technologies.

Related papers

• SAGE-LLM: Towards Safe and Generalizable LLM Controller with Fuzzy-CBF Verification and Graph-Structured Knowledge Retrieval for UAV Decision [46.089736018739295]
  Large Language Models (LLM) lack domain-specific UAV control knowledge and formal safety assurances.<n>This paper proposes a train-free two-layer decision architecture based on LLMs, integrating high-level safety planning with low-level precise control.
  arXiv  Detail & Related papers  (2026-02-27T06:41:04Z)
• Generative AI in Systems Engineering: A Framework for Risk Assessment of Large Language Models [0.8062120534124607]
  The increasing use of Large Language Models (LLMs) offers significant opportunities across the engineering lifecycle.<n>This paper introduces the LLM Risk Assessment Framework (LRF), a structured approach for evaluating the application of LLMs within Systems Engineering environments.
  arXiv  Detail & Related papers  (2026-02-04T09:30:11Z)
• STAMP/STPA informed characterization of Factors Leading to Loss of Control in AI Systems [0.0]
  We set out to provide a more structured framework for discussing and characterizing loss of control.<n>We use this framework to assist those responsible for the safe operation of AI-containing socio-technical systems to identify causal factors leading to loss of control.
  arXiv  Detail & Related papers  (2025-12-19T14:07:32Z)
• Event-Chain Analysis for Automated Driving and ADAS Systems: Ensuring Safety and Meeting Regulatory Timing Requirements [0.0]
  This paper presents a structured, White-Box methodology based on Event-Chain Modeling.<n>Unlike Black-Box approaches, Event-Chain Analysis offers transparent insights into the timing behavior of each functional component.<n>Our methodology enables the derivation, modeling, and validation of end-to-end timing constraints.
  arXiv  Detail & Related papers  (2025-11-22T15:22:05Z)
• Benchmarking and Studying the LLM-based Agent System in End-to-End Software Development [33.01897134024342]
  Development of LLM-based autonomous agents for end-to-end software development represents a significant paradigm shift in software engineering.<n>This work provides the community with a more realistic benchmark, a comprehensive evaluation framework, and crucial insights into the current capabilities and core challenges of software development agents.
  arXiv  Detail & Related papers  (2025-11-06T05:10:04Z)
• A Comprehensive Survey on Benchmarks and Solutions in Software Engineering of LLM-Empowered Agentic System [56.40989626804489]
  This survey provides the first holistic analysis of Large Language Models-powered software engineering.<n>We review over 150 recent papers and propose a taxonomy along two key dimensions: (1) Solutions, categorized into prompt-based, fine-tuning-based, and agent-based paradigms, and (2) Benchmarks, including tasks such as code generation, translation, and repair.
  arXiv  Detail & Related papers  (2025-10-10T06:56:50Z)
• SV-LLM: An Agentic Approach for SoC Security Verification using Large Language Models [8.912091484067508]
  We introduce SV-LLM, a novel multi-agent assistant system designed to automate and enhance system-on-chip (SoC) security verification.<n>By integrating specialized agents for tasks like verification question answering, security asset identification, threat modeling, test plan and property generation, vulnerability detection, and simulation-based bug validation, SV-LLM streamlines the workflow.<n>The system aims to reduce manual intervention, improve accuracy, and accelerate security analysis, supporting proactive identification and mitigation of risks early in the design cycle.
  arXiv  Detail & Related papers  (2025-06-25T13:31:13Z)
• MSDA: Combining Pseudo-labeling and Self-Supervision for Unsupervised Domain Adaptation in ASR [59.83547898874152]
  We introduce a sample-efficient, two-stage adaptation approach that integrates self-supervised learning with semi-supervised techniques.<n>MSDA is designed to enhance the robustness and generalization of ASR models.<n>We demonstrate that Meta PL can be applied effectively to ASR tasks, achieving state-of-the-art results.
  arXiv  Detail & Related papers  (2025-05-30T14:46:05Z)
• ASIL-Decomposition Based Resource Allocation Optimization for Automotive E/E Architectures [0.4143603294943439]
  We present an approach to automatically map software components to available hardware resources.<n>Compared to existing frameworks, our method provides a wider range of safety analyses in compliance with the ISO 26262 standard.<n>We formulate a multi-objective optimization problem to minimize both the development cost and the maximum execution times of critical function chains.
  arXiv  Detail & Related papers  (2025-05-10T15:48:29Z)
• Edge-Cloud Collaborative Computing on Distributed Intelligence and Model Optimization: A Survey [58.50944604905037]
  Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications.<n>Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these distributed systems.<n>This survey provides a structured tutorial on fundamental architectures, enabling technologies, and emerging applications.
  arXiv  Detail & Related papers  (2025-05-03T13:55:38Z)
• An LLM-enabled Multi-Agent Autonomous Mechatronics Design Framework [49.633199780510864]
  This work proposes a multi-agent autonomous mechatronics design framework, integrating expertise across mechanical design, optimization, electronics, and software engineering.<n> operating primarily through a language-driven workflow, the framework incorporates structured human feedback to ensure robust performance under real-world constraints.<n>A fully functional autonomous vessel was developed with optimized propulsion, cost-effective electronics, and advanced control.
  arXiv  Detail & Related papers  (2025-04-20T16:57:45Z)
• Prioritizing Software Requirements Using Large Language Models [3.9422957660677476]
  This article focuses on requirements engineering, typically seen as the initial phase of software development. The challenge of identifying requirements and satisfying all stakeholders within time and budget constraints remains significant. This study introduces a web-based software tool utilizing AI agents and prompt engineering to automate task prioritization.
  arXiv  Detail & Related papers  (2024-04-05T15:20:56Z)
• AIOps Solutions for Incident Management: Technical Guidelines and A Comprehensive Literature Review [0.29998889086656577]
  This study proposes an AIOps terminology and taxonomy, establishing a structured incident management procedure and providing guidelines for constructing an AIOps framework. The goal is to provide a comprehensive review of technical and research aspects in AIOps for incident management, aiming to structure knowledge, identify gaps, and establish a foundation for future developments in the field.
  arXiv  Detail & Related papers  (2024-04-01T17:32:22Z)
• On STPA for Distributed Development of Safe Autonomous Driving: An Interview Study [0.7851536646859475]
  System-Theoretic Process Analysis (STPA) is a novel method applied in safety-related fields like defense and aerospace. STPA assumes prerequisites that are not fully valid in the automotive system engineering with distributed system development and multi-abstraction design levels. This can be seen as a maintainability challenge in continuous development and deployment.
  arXiv  Detail & Related papers  (2024-03-14T15:56:02Z)
• Evaluating Model-free Reinforcement Learning toward Safety-critical Tasks [70.76757529955577]
  This paper revisits prior work in this scope from the perspective of state-wise safe RL. We propose Unrolling Safety Layer (USL), a joint method that combines safety optimization and safety projection. To facilitate further research in this area, we reproduce related algorithms in a unified pipeline and incorporate them into SafeRL-Kit.
  arXiv  Detail & Related papers  (2022-12-12T06:30:17Z)

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.