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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.† [ABSTRACT FROM AUTHOR]

Trustworthy artificial intelligence (Trusted AI) is of utmost importance when learning-enabled components (LECs) are used in autonomous, safety-critical systems. When reliant on deep learning, these systems need to address the reliability, robustness, and interpretability of learning models. In addition to developing strategies to address these concerns, appropriate software architectures are needed to coordinate LECs and ensure they deliver acceptable behavior even under uncertain conditions. This work describes Anunnaki, a model-driven framework comprising loosely-coupled modular services designed to monitor and manage LECs with respect to Trusted AI assurance concerns when faced with different sources of uncertainty. More specifically, the Anunnaki framework supports the composition of independent, modular services to assess and improve the resilience and robustness of AI systems. The design of Annunaki was guided by several key software engineering principles (e.g., modularity, composability, and reusability) in order to facilitate its use and maintenance to support different aggregate monitoring and assurance analysis tools for LESs and their respective data sets. We demonstrate Anunnaki on two autonomous platforms, a terrestrial rover, and an unmanned aerial vehicle. Our studies show how Anunnaki can be used to manage the operations of different autonomous learning-enabled systems with vision-based LECs while exposed to uncertain environmental conditions. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVDs) are a class of medical conditions characterized by impaired functionality of the heart and blood vessels, among which coronary heart disease, cerebrovascular diseases, and heart failure are prominent. These diseases account for approximately one-third of all deaths recorded worldwide. A significant challenge currently faced by clinicians is the early prediction of heart failure. The employment of Machine Learning techniques has emerged as a leading method in predicting patient survival who exhibit symptoms of heart failure. Nonetheless, achieving a significant level of accuracy remains a topic that demands further investigation. The present study introduces a novel approach that leverages Reinforcement Learning (RL) to enhance the performance of the artificial neural network (ANN) techniques for survival prediction by identifying the optimal configuration of model hyper-parameters (HPO). The proposed approach has been assessed using a conventional benchmark dataset comprising the medical records of 299 patients. The results were compared with recent ANN approaches working on the same dataset. The findings indicate that the proposed approaches enhance the performance of the ANN-based heart failure predictive model. [ABSTRACT FROM AUTHOR]

The Human Machine Teaming (HMT) paradigm focuses on supporting partnerships between humans and autonomous machines. HMT describes requirements for transparency, augmented cognition, and coordination that enable far richer partnerships than those found in typical human-on-the-loop and human-in-the-loop systems. Autonomous, self-adaptive systems in domains such as autonomous driving, robotics, and Cyber-Physical Systems, are often implemented using the MAPE-K feedback loop as the primary reference model. However, while MAPE-K enables fully autonomous behavior, it does not explicitly address the interactions that occur between humans and autonomous machines as intended by HMT. In this article, we, therefore, present the MAPE-KHMT framework, which utilizes runtime models to augment the monitoring, analysis, planning, and execution phases of the MAPE-K loop to support HMT despite the different operational cadences of humans and machines. We draw on examples from our own emergency response system of interactive, autonomous, small unmanned aerial systems to illustrate the application of MAPE-KHMT in both a simulated and physical environment, and we discuss how the various HMT models are connected and can be integrated into a MAPE-K solution. [ABSTRACT FROM AUTHOR]

Self-adaptation solutions need to periodically monitor, reason about, and adapt a running system. The adaptation step involves generating an adaptation strategy and applying it to the running system whenever an anomaly arises. In this article, we argue that rather than generating individual adaptation strategies, the goal should be to adapt the control logic of the running system in such a way that the system itself would learn how to steer clear of future anomalies, without triggering self-adaptation too frequently. Although the need for adaptation is never eliminated, especially noting the uncertain and evolving environment of complex systems, reducing the frequency of adaptation interventions is advantageous for various reasons, such as to increase performance and to make a running system more robust. We instantiate and empirically examine the preceding idea for software-defined networking—a key enabling technology for modern data centers and Internet of Things applications. Using genetic programming (GP), we propose a self-adaptation solution that continuously learns and updates the control constructs in the data-forwarding logic of a software-defined network. Our evaluation, performed using open source synthetic and industrial data, indicates that compared to a baseline adaptation technique that attempts to generate individual adaptations, our GP-based approach is more effective in resolving network congestion, and further, it reduces the frequency of adaptation interventions over time. In addition, we show that for networks with the same topology, reusing over larger networks the knowledge that is learned on smaller networks leads to significant improvements in the performance of our GP-based adaptation approach. Finally, we compare our approach against a standard data-forwarding algorithm from the network literature, demonstrating that our approach significantly reduces packet loss. [ABSTRACT FROM AUTHOR]

Self-adaptive systems can autonomously adjust their behavior in response to environmental changes. Nowadays, not only can these systems be engineered individually, but they can also be conceived as members of a family based on the approach of dynamic software product lines. Through systematic mapping, we build on the identified gaps in the variability management of self-adaptive systems; we propose a framework that improves the adaptive capability of self-adaptive systems through feature model generation, variation point generation, the selection of a variation point, and runtime variability management using deep learning and the monitor–analysis–plan–execute–knowledge (MAPE-K) control loop. We compute the permutation of domain features and obtain all the possible variation points that a feature model can possess. After identifying variation points, we obtain an adaptation rule for each variation point of the corresponding product line through a two-stage training of an artificial neural network. To evaluate our proposal, we developed a test case in the context of an air quality-based activity recommender system, in which we generated 11 features and 32 possible variations. The results obtained with the proof of concept show that it is possible to manage identifying new variation points at runtime using deep learning. Future research will employ generating and building variation points using artificial intelligence techniques. [ABSTRACT FROM AUTHOR]

The modern development approaches are establishing microservices and cloud computing as major trends to benefit the technological community. However, these technologies are often prone to multiple issues regarding parallel development by numerous parties, delivery strategies and resource allocation. This paper proposes a novel architecture for developing self-adaptive microservices, using Kubernetes through the Azure Container Apps, including a strategy that will complement the architecture to enhance the development of microservices and aiming to achieve a solution that allows the readers to deliver software faster, with more resilience, more scalable, and more cost-effective, depending as low as possible from human intervention to maintain and scale. The author will apply the acquired knowledge to propose and test an architecture for a real use case scenario, building a notifications service integrated with a complex cloud-based web application system. [ABSTRACT FROM AUTHOR]

This research presents a novel Self-Adaptive Waste Management System (SAWMS) that integrates advanced technology to address the pressing challenges of waste sorting and classification. SAWMS leverages Convolutional Neural Networks (CNNs) in conjunction with conveyor belt technology to achieve real-time object classification and self-training capabilities. The system utilizes sensors for object detection and a camera for image capture, enabling an accurate initial classification of waste objects into predefined categories such as food waste, metal, and plastic bottles. Notably, our proposed system sets itself apart by its unique ability to adapt and self-train based on classification errors, ensuring ongoing accuracy even in the face of changing waste compositions. Through dynamic adjustments of the conveyor belt's destination, it efficiently directs waste objects to their appropriate bins for disposal or recycling. This research demonstrates the potential of SAWMS to revolutionize waste management practices, offering an agile and sustainable solution to the evolving challenges of waste sorting and disposal. [ABSTRACT FROM AUTHOR]

A prosumer is a consumer who uses tiny, renewable electricity generation units and, in addition to consumption, can also generate electricity. With the increase in the number of prosumers in a power grid, it is expected that the paradigm of network utilization, at least at the distribution level, would change from centralized to decentralized utilization. In this new paradigm, microgrids are essential in the operation of the whole grid. The decentralized control of a stable network of microgrids (i.e., minimal power outages and fluctuations) is a significant challenge. In this paper, we present an architecture for decentralized control that consists of intelligent agents that manage the distribution network provided by the microgrids at the highest level and houses and their devices at the lowest level. The agents, managed by different private companies, dynamically organize themselves in units called holons, follow their defined policies, and can most tolerate network disruptions. In this architecture, self-adaptive agents will play a key role in sustaining network performance by controlling energy consumption and exchange (i.e., in the event of a shortage in a part of the distribution network). In the end, by simulating the architecture, the capabilities are shown. [ABSTRACT FROM AUTHOR]

There have been a number of approaches to employ neural networks in self-adaptive systems; in many cases, generic neural networks and deep learning are utilized for this purpose. When this approach is to be applied to improve an adaptation process initially driven by logical adaptation rules, the problem is that (1) these rules represent a significant and tested body of domain knowledge, which may be lost if they are replaced by a neural network, and (2) the learning process is inherently demanding given the black-box nature and the number of weights in generic neural networks to be trained. In this paper, we introduce the rule-specific neural network method that makes it possible to transform the guard of an adaptation rule into a rule-specific neural network, the composition of which is driven by the structure of the logical predicates in the guard. Our experiments confirmed that the black box effect is eliminated, the number of weights is significantly reduced, and much faster learning is achieved whilst the accuracy is preserved. This text is an extended version of the paper presented at the ISOLA 2022 conference (Bureš et al. in Proceedings of ISOLA 2022, Rhodes, Greece, pp. 215–230, 2022). [ABSTRACT FROM AUTHOR]

Increasingly, safety-critical systems include artificial intelligence and machine learning components (i.e., learning-enabled components (LECs)). However, when behavior is learned in a training environment that fails to fully capture real-world phenomena, the response of an LEC to untrained phenomena is uncertain and therefore cannot be assured as safe. Automated methods are needed for self-assessment and adaptation to decide when learned behavior can be trusted. This work introduces a model-driven approach to manage self-adaptation of a learning-enabled system (LES) to account for run-time contexts for which the learned behavior of LECs cannot be trusted. The resulting framework enables an LES to monitor and evaluate goal models at run time to determine whether or not LECs can be expected to meet functional objectives and enables system adaptation accordingly. Using this framework enables stakeholders to have more confidence that LECs are used only in contexts comparable to those validated at design time. [ABSTRACT FROM AUTHOR]

The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach's effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance. [ABSTRACT FROM AUTHOR]

This research presents a novel Self-Adaptive Waste Management System (SAWMS) that integrates advanced technology to address the pressing challenges of waste sorting and classification. SAWMS leverages Convolutional Neural Networks (CNNs) in conjunction with conveyor belt technology to achieve real-time object classification and self-training capabilities. The system utilizes sensors for object detection and a camera for image capture, enabling an accurate initial classification of waste objects into predefined categories such as food waste, metal, and plastic bottles. Notably, our proposed system sets itself apart by its unique ability to adapt and self-train based on classification errors, ensuring ongoing accuracy even in the face of changing waste compositions. Through dynamic adjustments of the conveyor belt’s destination, it efficiently directs waste objects to their appropriate bins for disposal or recycling. This research demonstrates the potential of SAWMS to revolutionize waste management practices, offering an agile and sustainable solution to the evolving challenges of waste sorting and disposal.

Adaptive systems manage and regulate the behavior of devices or other systems using control loops to automatically adjust the value of some measured variables to equal the value of a desired set-point. These systems normally interact with physical parts or operate in physical environments, where uncertainty is unavoidable. Traditional approaches to manage that uncertainty use either robust control algorithms that consider bounded variations of the uncertain variables and worst-case scenarios or adaptive control methods that estimate the parameters and change the control laws accordingly. In this article, we propose to include the sources of uncertainty in the system models as first-class entities using random variables to simulate adaptive and control systems more faithfully, including not only the use of random variables to represent and operate with uncertain values but also to represent decisions based on their comparisons. Two exemplar systems are used to illustrate and validate our proposal. [ABSTRACT FROM AUTHOR]

Self-adaptive software can assess and modify its behavior when the assessment indicates that the program is not performing as intended or when improved functionality or performance is available. Since the mid-1960s, system adaptivity has been extensively researched, and during the last decade, many application areas and technologies involving self-adaptation have gained prominence. All of these efforts have in common the introduction of self-adaptability through software. Thus, it is essential to investigate systematic software engineering methods to create self-adaptive systems that may be used across different domains. The primary objective of this research is to summarize current advances in awareness requirements for adaptive strategies and their performance management based on an examination of state-of-the-art methods described in the literature. This paper reviews self-adaptive systems in the context of requirement awareness and summarizes the most common methodologies applied. At first glance, it examines the previous surveys and works about self-adaptive systems. Afterward, it classifies the current self-adaptive systems based on six criteria. Then, it presents performance management in the current adaptive systems and then evaluates the most common self-adaptive approaches. Lastly, the self-adaptive models are evaluated based on four concepts (requirements description, monitoring, relationship, dependency/impact, and tools). [ABSTRACT FROM AUTHOR]

The dramatic growth of smart devices and the Internet of things (IoT) has increased the volume of exchanges and data on the web. The centralized and traditional architecture of cloud computing does not meet the demands of users and proper implementation of latency-sensitive applications due to latency and mass demands of IoT applications that have different needs compared to existing applications. As a result, edge computing has been presented for collecting and processing of data generated by these objects, which facilitates data processing with low latency and close to users at the edge of the network. Its main purpose is to bring computational resources and storage close to the end-user on the network. As far as the storage capacity of fog nodes is limited, the proper use of fog node resources significantly influences their performance. In this paper, a framework based on control MAPE-K loop has been used to obtain the optimal state in workload balance. The users' workload forecasting model is a combination of linear regression and support vector regression methods and offers better performance compared to the conventional reactive self-assessment methods. In the planning phase, a fuzzy self-learning algorithm is used to determine the automated scale of resource provisioning policy. By comparing three criteria of load delay, cost, and amount of consumed energy in the proposed method and recent works, the proposed method has been able to balance all three criteria optimally. [ABSTRACT FROM AUTHOR]

For realistic self-adaptive systems, multiple quality attributes need to be considered and traded off against each other. These quality attributes are commonly encoded in a utility function, for instance, a weighted sum of relevant objectives. Utility functions are typically subject to a set of constraints, i.e., hard requirements that should not be violated by the system. The research agenda for requirements engineering for self-adaptive systems has raised the need for decision-making techniques that consider the trade-offs and priorities of multiple objectives. Human stakeholders need to be engaged in the decision-making process so that constraints and the relative importance of each objective can be correctly elicited. This paper presents a method that supports multiple stakeholders in eliciting constraints, prioritizing quality attributes, negotiating priorities, and giving input to define utility functions for self-adaptive systems. We developed tool support in the form of a blackboard system that aggregates information by different stakeholders, detects conflicts, proposes mechanisms to reach an agreement, and generates a utility function. We performed a think-aloud study with 14 participants to investigate negotiation processes and assess the approach's understandability and user satisfaction. Our study sheds light on how humans reason about and how they negotiate around quality attributes. The mechanisms for conflict detection and resolution were perceived as very useful. Overall, our approach was found to make the process of utility function definition more understandable and transparent. [ABSTRACT FROM AUTHOR]

IoT infrastructures are becoming increasingly more difficult to manage. One of the main issues is the high volatility present in the infrastruture, which increasingly demands self-adaptive solutions. As a proposal to handle this challenge, this paper presents STEER (Sdn-based inTEnt drivEn iot netwoRks), a new approach for the dynamic adaptation of IoT networks for indoor monitoring applications, based on the unification of Intent-Driven Networks (IDN) and Software-Defined Networks (SDN). Particularly, we explore the ability of IDNs to dynamically interpret an application's intent, using an IDN-based mediator attached to an SDN-controller to autonomously adapt the IoT network behavior at runtime, thus realizing the intent according to the current operating context of the network. We demonstrate the approach using a representative application scenario related to IoT indoor environment monitoring in the domain of indoor air quality monitoring. The experiments allowed us to validate the applicability of the approach and show the system-wide effect of dynamic adaptation to the current operating environment on improving performance according to the metric under consideration, in this case, the number of application-level messages exchanged in the network. [ABSTRACT FROM AUTHOR]

In presence of multiple objectives to be optimized in Search-Based Software Engineering (SBSE), Pareto search has been commonly adopted. It searches for a good approximation of the problem’s Pareto-optimal solutions, from which the stakeholders choose the most preferred solution according to their preferences. However, when clear preferences of the stakeholders (e.g., a set of weights that reflect relative importance between objectives) are available prior to the search, weighted search is believed to be the first choice, since it simplifies the search via converting the original multi-objective problem into a single-objective one and enables the search to focus on what only the stakeholders are interested in. This article questions such a “weighted search first” belief. We show that the weights can, in fact, be harmful to the search process even in the presence of clear preferences. Specifically, we conduct a large-scale empirical study that consists of 38 systems/projects from three representative SBSE problems, together with two types of search budget and nine sets of weights, leading to 604 cases of comparisons. Our key finding is that weighted search reaches a certain level of solution quality by consuming relatively less resources at the early stage of the search; however, Pareto search is significantly better than its weighted counterpart the majority of the time (up to 77% of the cases), as long as we allow a sufficient, but not unrealistic search budget. This is a beneficial result, as it discovers a potentially new “rule-of-thumb” for the SBSE community: Even when clear preferences are available, it is recommended to always consider Pareto search by default for multi-objective SBSE problems, provided that solution quality is more important. Weighted search, in contrast, should only be preferred when the resource/search budget is limited, especially for expensive SBSE problems. This, together with other findings and actionable suggestions in the article, allows us to codify pragmatic and comprehensive guidance on choosing weighted and Pareto search for SBSE under the circumstance that clear preferences are available. [ABSTRACT FROM AUTHOR]

Self-adaptive systems (SASs) are increasingly leveraging autonomy in their decision-making to manage uncertainty in their operating environments. A key problem with SASs is ensuring their requirements remain satisfied as they adapt. The trade-off analysis of the non-functional requirements (NFRs) is key to establish balance among them. Further, when performing the trade-offs it is necessary to know the importance of each NFR to be able to resolve conflicts among them. Such trade-off analyses are often built upon optimisation methods, including decision analysis and utility theory. A problem with these techniques is that they use a single-scalar utility value to represent the overall combined priority for all the NFRs. However, this combined scalar priority value may hide information about the impacts of the environmental contexts on the individual NFRs' priorities, which may change over time. Hence, there is a need for support for runtime, autonomous reasoning about the separate priority values for each NFR, while using the knowledge acquired based on evidence collected. In this paper, we propose Pri-AwaRE, a self-adaptive architecture that makes use of Multi-Reward Partially Observable Markov Decision Process (MR-POMDP) to perform decision-making for SASs while offering awareness of NFRs' priorities. MR-POMDP is used as a priority-aware runtime specification model to support runtime reasoning and autonomous tuning of the distinct priority values of NFRs using a vector-valued reward function. We also evaluate the usefulness of our Pri-AwaRE approach by applying it to two substantial example applications from the networking and IoT domains. [ABSTRACT FROM AUTHOR]

The Industrial Revolution drives the digitization of society and industry, entailing Cyber-Physical Systems (CPSs) that form ecosystems where system owners and third parties share responsibilities within and across industry domains. Such ecosystems demand smart CPSs that continuously align their architecture and governance to the concerns of various stakeholders, including developers, operators, and users. In order to satisfy short- and long-term stakeholder concerns in a continuously evolving operational context, this work proposes self-adaptive software models that promote DevOps for smart CPS. Our architectural approach extends to the embedded system layer and utilizes embedded and interconnected Digital Twins to manage change effectively. Experiments conducted on industrial embedded control units demonstrate the approach’s effectiveness in achieving sub-millisecond real-time closed-loop control of CPS assets and the simultaneous high-fidelity twinning (i.e., monitoring) of asset states. In addition, the experiments show practical support for the adaptation and evolution of CPS through the dynamic reconfiguring and updating of real-time control services and communication links without downtime. The evaluation results conclude that, in particular, the embedded Digital Twins can enhance CPS smartness by providing service-oriented access to CPS data, monitoring, adaptation, and control capabilities. Furthermore, the embedded Digital Twins can facilitate the seamless integration of these capabilities into current and future industrial service ecosystems. At the same time, these capabilities contribute to implementing emerging industrial services such as remote asset monitoring, commissioning, and maintenance.

Context: Dynamic software product lines (DSPLs) have considerably increased their adoption for variability management for self-adaptive systems. The most widely used models for managing the variability of DSPLs are the MAPE-K control loop and context-aware feature models (CFMs). Aim: In this paper, we review and synthesize evidence of using variability constraint approaches, methodologies, and challenges for DSPL. Method: We conducted a systematic mapping, including three research questions. This study included 84 papers published from 2010 to 2021. Results: The main results show that open-dynamic variability shows a presence in 57.1% of the selected papers, and on the other hand, closed-dynamic variability appears in 38.1%. The most commonly used methodology for managing a DSPL environment is based on proprietary architectures (60.7%), where the use of CFMs predominates. For open-dynamic variability approaches, the MAPE-K control loop is mainly used. The main challenges in DSPL management are based on techniques (28.6%) and open variation (21.4%). Conclusions: Open-dynamic variability has prevailed over the years as the primary approach to managing variability in DSPL, where its primary methodology is the MAPE-K control loop. Response RQ3 requires further review. [ABSTRACT FROM AUTHOR]

Modern software systems are intricate and operate in highly dynamic environments for which few assumptions can be made at design-time. This setting has sparked an interest in solutions that use a runtime model which reflects the system state and operational context to monitor and adapt the system in reaction to changes during its runtime. Few solutions focus on the evolution of the model over time, i.e., its history, although history is required for monitoring temporal behaviors and may enable more informed decision-making. One reason is that handling the history of a runtime model poses an important technical challenge, as it requires tracing a part of the model over multiple model snapshots in a timely manner. Additionally, the runtime setting calls for memory-efficient measures to store and check these snapshots. Following the common practice of representing a runtime model as a typed attributed graph, we introduce a language which supports the formulation of temporal graph queries, i.e., queries on the ordering and timing in which structural changes in the history of a runtime model occurred. We present a querying scheme for the execution of temporal graph queries over history-aware runtime models. Features such as temporal logic operators in queries, the incremental execution, the option to discard history that is no longer relevant to queries, and the in-memory storage of the model, distinguish our scheme from relevant solutions. By incorporating temporal operators, temporal graph queries can be used for runtime monitoring of temporal logic formulas. Building on this capability, we present an implementation of the scheme that is evaluated for runtime querying, monitoring, and adaptation scenarios from two application domains. [ABSTRACT FROM AUTHOR]