Context: Responsibility gaps, long-recognized challenges in socio-technical systems where accountability becomes diffuse or ambiguous, have become increasingly pronounced in GenAI-enabled software. The generative and adaptive nature complicates how human oversight and responsibility are specified, delegated, and traced. Existing requirements engineering (RE) approaches remain limited in addressing these phenomena, revealing conceptual, methodological, and artifact-level research gaps.. Objective: This study aims to analyze these research gaps in the context of GenAI-enabled software systems. It seeks to establish a coherent perspective for a systematic analysis of responsibility gaps from a human oversight requirements standpoint, encompassing how these responsibility gaps should be conceptualized, identified, and represented throughout the RE process. Methods: The proposed design methodology is structured across three analytical layers. At the conceptualization layer, it establishes a conceptual framing that defines the key elements of responsibility across the human and system dimensions and explains how potential responsibility gaps emerge from their interactions. At the methodological layer, it introduces a deductive pipeline for identifying responsibility gaps by analyzing interactions between these dimensions and deriving corresponding oversight requirements within established RE frameworks. At the artifact layer, it formalizes the results in a Deductive Backbone Table, a reusable representation that traces the reasoning path from responsibility gaps identification to human oversight requirements derivation. Results: A user study compared the proposed methodology with a baseline goal-oriented RE across two scenarios. Evaluation across six dimensions indicated clear improvements of the proposed methodology, confirming its effectiveness in addressing three research gaps.

翻译：背景：责任鸿沟作为社会技术系统中长期存在的挑战，即责任变得分散或模糊，在生成式人工智能（GenAI）赋能的软件中日益凸显。其生成性与自适应性使得人类监督与责任的界定、委派和追溯变得复杂。现有的需求工程（RE）方法在应对这些现象时仍存在局限，揭示了概念、方法和制品层面的研究空白。目标：本研究旨在分析生成式人工智能赋能软件系统中的这些研究空白，从人类监督需求的角度为系统分析责任鸿沟建立连贯的视角，涵盖责任鸿沟在需求工程过程中应如何概念化、识别与表征。方法：所提出的设计方法学构建于三个分析层面。在概念化层面，建立了一个概念框架，定义了人与系统维度间的责任关键要素，并阐释了潜在责任鸿沟如何从其交互中产生。在方法论层面，引入了一个演绎式流程，通过分析这些维度间的交互，并在既有需求工程框架内推导相应的监督需求，以识别责任鸿沟。在制品层面，将结果形式化为“演绎主干表”，这是一种可复用的表征形式，可追溯从责任鸿沟识别到人类监督需求推导的推理路径。结果：一项用户研究在两个场景下比较了所提方法与基线目标导向需求工程。六个维度的评估表明，所提方法具有明显改进，证实了其在解决三个研究空白方面的有效性。