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

• Published in: ACM transactions on autonomous and adaptive systems Vol. 19; no. 3; pp. 1 - 60
• Main Authors: Li, Jialong, Zhang, Mingyue, Li, Nianyu, Weyns, Danny, Jin, Zhi, Tei, Kenji
• Format: Journal Article
• Language: English
• Published: New York, NY ACM 30.09.2024
• Subjects: Artificial intelligence; Computer Science; Computing methodologies; Datavetenskap; diffusion model; Generative AI; Human computer interaction (HCI); Human-centered computing; Large Language Model; MAPE; Self-Adaptive Systems; Software and its engineering; Software creation and management; survey; survey; MAPE; Generative AI; diffusion model; Self-Adaptive Systems; Large Language Model
• ISSN: 1556-4665; 1556-4703; 1556-4703
• DOI: 10.1145/3686803

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 article 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.†