发表机构
Tampere University; University of Jyväskylä（坦佩雷大学; 于韦斯屈莱大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出一个自主性感知元模型，将自主性视为情境相关的权威分配，通过四个维度支持动态权威分配，为AI优先的软件工程方法奠定概念基础。
AI 中文摘要
人工智能（AI）正在将软件工程从工具支持的过程转向AI优先的协作，在这种协作中，权威在人类和人工参与者之间动态分配。然而，现有的方法工程方法假设静态的、以人为中心的控制，并且对明确捕获不断演变的自主性的支持有限。本文提出了一个自主性感知方法工程的愿景，通过提出一个元模型，将自主性视为不是参与者的固定属性，而是由任务、上下文和协作模式决定的派生的、情境相关的权威分配。该元模型通过四个权威维度形式化自主性：任务执行、任务分解、任务发起和协作重构。通过一个多智能体需求分析工具的分析性实例化，我们展示了该元模型如何支持动态权威分配。这项工作为治理感知、可适应和AI优先的软件工程方法提供了概念基础。
英文摘要
Artificial Intelligence (AI) is shifting software engineering from tool-supported processes towards AI-first collaboration, where authority is dynamically distributed across human and artificial actors. However, existing method engineering approaches assume static, human-centric control and provide limited support explicitly capturing evolving autonomy. This paper presents a vision for autonomy-aware method engineering by proposing a metamodel that treats autonomy not as a fixed property of an actor, but as a derived, situation-dependent authority assignment determined by task, context, and collaboration pattern. The metamodel formalizes autonomy through four authority dimensions: task execution, task decomposition, task initiation, and collaboration reconfiguration. Through an analytical instantiation with a multi-agent requirements analysis tool, we illustrate how the metamodel supports dynamic authority assignment. This work provides a conceptual foundation for governance-aware, adaptable, and AI-first software engineering methods.
