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. ^†

Large Language Models (LLMs), with their abilities in knowledge acquisition and reasoning, can potentially enhance the various aspects of Self-adaptive Systems (SAS). Yet, the potential of LLMs in SAS remains largely unexplored and ambiguous, due to the lack of literature from flagship conferences or journals in the field, such as SEAMS and TAAS. The interdisciplinary nature of SAS suggests that drawing and integrating ideas from related fields, such as software engineering and autonomous agents, could unveil innovative research directions for LLMs within SAS. To this end, this paper reports the results of a literature review of studies in relevant fields, summarizes and classifies the studies relevant to SAS, and outlines their potential to specific aspects of SAS. Literature classification: www.github.com/545659928/LLM4SAS

Security attacks present unique challenges to the design of self-adaptation mechanism for software-intensive systems due to the adversarial nature of the environment. Game-theoretical approaches have been explored in security to model malicious behaviors and design reliable defense for the system in a mathematically grounded manner. However, modeling the system as a single player, as done in prior works, is insufficient for the system under partial compromise and for the design of fine-grained defensive policies where the rest of the system with autonomy can cooperate to mitigate the impact of attacks. To address such issues, we propose a new self-adaptation framework incorporating Bayesian game theory and model the defender (i.e., the system) at the granularity of components. Under security attacks, the architecture model of the system is automatically translated, by the proposed translation process with designed algorithms, into a multi-player Bayesian game. This representation allows each component to be modeled as an independent player, while security attacks are encoded as variant types for the components. By solving for pure equilibrium (i.e., adaptation response), the system’s optimal defensive strategy is dynamically computed, enhancing system resilience against security attacks by maximizing system utility. We validate the effectiveness of our framework through two sets of experiments using generic benchmark tasks tailored for the security domain. Additionally, we exemplify the practical application of our approach through a real-world implementation in the Secure Water Treatment System to demonstrate the applicability and potency in mitigating security risks.

External human-machine interfaces (eHMI) are anticipated to enhance pedestrian interactions with automated vehicles (AVs) and increase social acceptance. While previous studies have focused on enhancing eHMI design by exploring various modalities and color preferences, identifying the optimal eHMI location on vehicles remains a significant challenge. Existing research has begun to explore different display locations for eHMI, but a thorough examination of the factors related to vehicle type, such as vehicle size, that influence pedestrian preferences is still lacking. Therefore, we conducted a Virtual Reality user study to assess pedestrian reactions to various AVs equipped with eHMI in different positions. Our findings reveal that larger vehicles are perceived as less safe by pedestrians, yet their increased visibility from a distance, due to longer light bands, impacts pedestrians’ crossing decisions. Additionally, the location and height of the eHMI relative to eye level are crucial, with inappropriate positioning leading to diminished effectiveness.

In the realm of autonomous vehicles, dynamic user preferences are critical yet challenging to accommodate. Existing methods often misrepresent these preferences, either by overlooking their dynamism or overburdening users as humans often find it challenging to express their objectives mathematically. The previously introduced framework, which interprets dynamic preferences as inherent uncertainty and includes a “human-on-the-loop” mechanism enabling users to give feedback when dissatisfied with system behaviors, addresses this gap. In this study, we further enhance the approach with a user study of 20 participants, focusing on aligning system behavior with user expectations through feedback-driven adaptation. The findings affirm the approach’s ability to effectively merge algorithm-driven adjustments with user complaints, leading to improved participants’ subjective satisfaction in autonomous systems.

In cyber-physical systems, physical and software components are deeply intertwined, blurring the boundaries between the cyber and physical worlds. Perceiving environmental information is a prerequisite for a cyber-physical system to be reliable and adaptive to the environment. However, the intrinsically open and dynamic nature of the environment brings challenges to the systematic realization of environmental-perception. This article regards environmental-perception as a first-class entity with self-maintenance and abstracts it into three levels: data capture, context awareness, and situation identification. Then, a five-tier reference architecture is proposed, which not only explicitly defines the perception ability at different abstraction levels, but also provides for the storage and management of environmental information, thus facilitating information reuse and the adaptation ability of cyber-physical systems. We evaluate the reference architecture with a case study on a widely used smart room environment, demonstrating its broad applicability and the ability to reuse environmental information. We also performed a survey on the proposed reference architecture to reveal its industrial motivations and benefits.