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Runtime Advocates: A Persona-Driven Framework for Requirements@Runtime Decision Support

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arxiv 2505.04551 v1 pith:LPXY4E3U submitted 2025-05-07 cs.SE cs.HCcs.MA

classification cs.SEcs.HCcs.MA
keywords personasrequirementsruntimeframeworkadvocateconditionsethicalevolving
verification ladder T0 review T1 audit T2 compute T3 formal
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Complex systems, such as small Uncrewed Aerial Systems (sUAS) swarms dispatched for emergency response, often require dynamic reconfiguration at runtime under the supervision of human operators. This introduces human-on-the-loop requirements, where evolving needs shape ongoing system functionality and behaviors. While traditional personas support upfront, static requirements elicitation, we propose a persona-based advocate framework for runtime requirements engineering to provide ethically informed, safety-driven, and regulatory-aware decision support. Our approach extends standard personas into event-driven personas. When triggered by events such as adverse environmental conditions, evolving mission state, or operational constraints, the framework updates the sUAS operator's view of the personas, ensuring relevance to current conditions. We create three key advocate personas, namely Safety Controller, Ethical Governor, and Regulatory Auditor, to manage trade-offs among risk, ethical considerations, and regulatory compliance. We perform a proof-of-concept validation in an emergency response scenario using sUAS, showing how our advocate personas provide context-aware guidance grounded in safety, regulatory, and ethical constraints. By evolving static, design-time personas into adaptive, event-driven advocates, the framework surfaces mission-critical runtime requirements in response to changing conditions. These requirements shape operator decisions in real time, aligning actions with the operational demands of the moment.

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Cited by 1 Pith paper

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Cognitive Guardrails for Open-World Decision Making in Autonomous Drone Swarms

    cs.RO 2025-05 conditional novelty 5.0 of 10

    CAIRN integrates LLM-based clue reasoning with a Bayesian strategy model and cognitive guardrails to guide autonomous drone swarms in open-world search-and-rescue.

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