Shuo Liu | Search arXiv

arXiv2026

Towards Requirements Engineering for GenAI-Enabled Software: Bridging Responsibility Gaps through Human Oversight Requirements

Context: Responsibility gaps, long-recognized challenges in socio-technical systems where accountability becomes diffuse or ambiguous, have become increasingly pronounced in GenAI-enabled software. This study 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 requirements engineering 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 requirements engineering 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 requirements engineering approach. Participant-perception results indicated generally favorable views of the enhanced approach across the evaluated dimensions, while expert ratings of the generated requirement artifacts showed higher mean scores for six of the seven artifact-quality items, with the strongest descriptive improvements observed for consistency and representational traceability.

cs.SE↗

arXiv2026

Imagine Before Retrieval: Prospective Skill Retrieval for LLM Agents

Skill retrieval has recently emerged as a promising paradigm for identifying the desirable execution guidelines from the skill gallery, thus equipping large language model (LLM) agents with the procedural knowledge to accomplish the specified task. To this end, most existing methods customize the retrieval model or reconfigure the retrieval pipeline to prioritize skills that are most semantically relevant to the task query. However, we empirically reveal that task queries and skills are naturally formulated from different perspectives, namely, objective-oriented and procedural-oriented, leading to an under-explored problem termed Query--Skill Misalignment (QSM). Clearly, it is daunting and even impossible to associate the desirable skills in the context of QSM, thus hindering the agent from correctly executing the task. As a remedy, inspired by human prospective cognition, we propose SkillDreamer, a novel framework to alleviate the negative impact of QSM problem. In brief, SkillDreamer first infers the capabilities necessary for task execution, then imagines how to realize these capabilities by generating pseudo skills, and finally leverages such prospective information to bridge the gap between objective-oriented task queries and execution-oriented skills. Extensive experiments on SkillRet and SkillUsage not only verify the effectiveness of SkillDreamer in both skill retrieval and end-to-end task execution, but also demonstrate its generalizability across diverse retrieval models and pipelines. The code will be released upon acceptance.

cs.IR↗