Self-adaptive systems (SASs) are designed to handle changes and uncertainties through a feedback loop with four core functionalities: monitoring, analyzing, planning, and execution. Recently, generative artificial intelligence (GenAI), especially the area of large language models, has shown impressive performance in data comprehension and logical reasoning. These capabilities are highly aligned with the functionalities required in SASs, suggesting a strong potential to employ GenAI to enhance SASs. However, the specific benefits and challenges of employing GenAI in SASs remain unclear. Yet, providing a comprehensive understanding of these benefits and challenges is complex due to several reasons: limited publications in the SAS field, the technological and application diversity within SASs, and the rapid evolution of GenAI technologies. To that end, this paper aims to provide researchers and practitioners a comprehensive snapshot that outlines the potential benefits and challenges of employing GenAI's within SAS. Specifically, we gather, filter, and analyze literature from four distinct research fields and organize them into two main categories to potential benefits: (i) enhancements to the autonomy of SASs centered around the specific functions of the MAPE-K feedback loop, and (ii) improvements in the interaction between humans and SASs within human-on-the-loop settings. From our study, we outline a research roadmap that highlights the challenges of integrating GenAI into SASs. The roadmap starts with outlining key research challenges that need to be tackled to exploit the potential for applying GenAI in the field of SAS. The roadmap concludes with a practical reflection, elaborating on current shortcomings of GenAI and proposing possible mitigation strategies.

翻译：自适应系统（SASs）旨在通过包含监控、分析、规划与执行四大核心功能的反馈循环来应对变化与不确定性。近年来，生成式人工智能（GenAI），尤其是大语言模型领域，在数据理解与逻辑推理方面展现出卓越性能。这些能力与SASs所需的功能高度契合，表明利用GenAI增强SASs具有巨大潜力。然而，在SASs中应用GenAI的具体优势与挑战尚不明确。由于SAS领域相关文献有限、SASs内部技术与应用场景的多样性，以及GenAI技术的快速演进，全面理解这些优势与挑战具有复杂性。为此，本文旨在为研究人员与实践者提供一个系统性综述，阐明在SASs中应用GenAI的潜在优势与挑战。具体而言，我们汇集、筛选并分析了来自四个不同研究领域的文献，将其归纳为两大潜在优势类别：（i）围绕MAPE-K反馈循环特定功能提升SASs的自主性；（ii）在人在回路场景中改善人类与SASs的交互。基于研究，我们提出了一个突出GenAI与SASs融合挑战的研究路线图。该路线图首先阐述了在SAS领域应用GenAI需攻克的关键研究挑战，最后通过实践反思，详细探讨了当前GenAI的局限性并提出了可能的缓解策略。