Generative AI for Self-Adaptive Systems: State of the Art and Research Roadmap

2025-12-04

AI Summary

Despite growing interest in integrating generative AI (GenAI) into self-adaptive systems (SASs), its advantages and challenges remain poorly understood. To address this gap, this study conducts the first cross-domain systematic literature review spanning software engineering, human-computer interaction, autonomous systems, and AI—augmented by large language model–assisted data analysis and logical reasoning—to assess GenAI’s technical fit within each component of the MAPE-K feedback loop. We propose a novel dual-dimensional framework—“autonomy enhancement” and “human-AI collaboration”—to systematically characterize GenAI’s core strengths (e.g., dynamic modeling, intent understanding, policy generation) and critical limitations (e.g., explainability, real-time responsiveness, trustworthiness assurance). Finally, we derive a forward-looking research roadmap covering technical challenges, validation methodologies, and practical implementation pathways—providing a cohesive foundation for both theoretical advancement and industrial deployment of GenAI-powered SASs.

Technology Category

• Natural Language Processing: Generation
• Multiagent Systems: Agent/AI Theories and Architectures
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Social Networks and Social Media: Generative AI / large language models and their impact on social systems
• Search and Retrieval-Augmented AI: Agentic search
• Responsible Web: Machine-in-the-loop, human agency and autonomy

Abstract

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.

Problem

• Explores GenAI's potential to enhance self-adaptive systems' autonomy.
• Identifies benefits and challenges of integrating GenAI into SAS.
• Proposes a research roadmap for applying GenAI in SAS.

Innovation

• Using generative AI to enhance self-adaptive systems' feedback loop.
• Applying large language models for monitoring and planning functions.
• Integrating GenAI to improve human-system interaction in loops.