ICSE

In its quest for approaches to taming uncertainty in self-adaptive systems (SAS), the research community has largely focused on solutions that adapt the SAS architecture or behaviour in response to uncertainty. By comparison, solutions that reduce the ...

In smart Cyber-Physical Systems (sCPS), a critical challenge lies in task planning under uncertainty. There is a broad body of work in the area with approaches able to deal with different classes of constraints (e.g., ordering, structural) and ...

Multi-robot systems (MRS) have gained interest as a versatile paradigm for complex task execution across various domains such as healthcare, logistics, and maintenance. Often, they are called to operate in variable and dynamic environments, which makes ...

Sources of uncertainty in adaptive systems are rarely independent, and their interaction can affect the attainment of system goals in unpredictable ways. Despite ample work on "taming" uncertainty, the research community has devoted little attention to ...

Small Uncrewed Aerial Systems (sUAS), commonly referred to as drones, have become ubiquitous in many domains. Examples range from drones taking part in search-and-rescue operations to drones being used for delivering medical supplies or packages. As sUAS ...

Smart Cyber-Physical Systems (sCPS) operate in dynamic and uncertain environments, where anticipation to adverse situations is crucial and decentralization is often necessary due to e.g., scalability issues. Addressing the limitations related to the lack ...

Smart Cyber-Physical Systems (sCPS) operate under dynamic, harsh conditions and limited observability of their operation environment. To facilitate the evaluation of distributed CPS where adaptation is affected by partial observability, this paper ...

Aerial drones are increasingly being considered as a valuable tool for inspection in safety critical contexts. Nowhere is this more true than in mining operations which present a dynamic and dangerous environment for human operators. Drones can be ...

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 ...

A/B testing is a common approach used in industry to facilitate innovation through the introduction of new features or the modification of existing software. Traditionally, A/B tests are administrated manually and conducted sequentially, with each ...

With the emergence of autonomous vehicles comes requirements on adequate and rigorous testing techniques, particularly as systems continuously adapt to changing environments. Scenario-based, simulated testing is one approach that has received attention, ...

Swarm-based algorithms are one of the most popular bio-inspired algorithms inspired by animals' collective behavior. In this position paper, I discuss the benefits of how the self-adaptive community could benefit from considering bio-inspired algorithms ...

Nature has an excellent track record in solving problems, and while biological inspired approaches draw inspiration from nature, they should not emulate it blindly. What works for nature may not work for computer systems - bio-inspired computing comes to ...

Federated Learning (FL) has emerged as a decentralised machine learning paradigm for distributed systems, particularly in edge and IoT environments. However, ensuring fault tolerance and self-recovery in such scenarios remains challenging, because of the ...

Cyber-physical systems (CPS) are subject to environmental uncertainties such as adverse operating conditions, malicious attacks, and hardware degradation. These uncertainties may lead to failures that put the system in a sub-optimal or unsafe state. ...

Self-healing systems depend on following a set of predefined instructions to recover from a known failure state. Failure states are generally detected based on domain specific specialized metrics. Failure fixes are applied at predefined application hooks ...

Addressing runtime uncertainties in Machine Learning-Enabled Systems (MLS) is crucial for maintaining Quality of Service (QoS). The Machine Learning Model Balancer is a concept that addresses these uncertainties by facilitating dynamic ML model switching,...

The recent trend of uncontrolled spread of infectious diseases has resulted in severe disruption to society on a worldwide scale. One of the causes is represented by public transportation services that contribute to the spread of an epidemic, which has ...

This paper introduces RAMSES, an exemplar tailored for both practitioners and researchers working on self-adaptive microservice applications. By emphasizing a clear separation of concerns between the application and its adaptation logic, RAMSES realizes ...

Microservices architecture offers various benefits, including granularity, flexibility, and scalability. A crucial feature of this architecture is the ability to autoscale microservices, i.e., adjust the number of replicas and/or manage resources. ...

Digital twins, which are increasingly adopted in industry, are model-centric systems used to improve the behavior of a twinned physical system. Seen as a whole, this system has several layers of self-adaptation: first, the digital twin manages its ...

Self-adaptive systems are able to adapt themselves according to changing contextual conditions to ensure a set of predefined objectives (e.g., certain non-functional requirements like reliability) is reached. For this, they perform adaptation actions ...

Self-adaptive systems increasingly rely on black-box predictive models (e.g., Neural Networks) to make decisions and steer adaptations. The lack of transparency of these models makes it hard to explain adaptation decisions and their possible effects on ...

Recent advances in generative AI and machine learning have stirred up fears about the unbridled adoption of autonomous, self-adaptive decision mechanisms in socio-technical systems. This vision paper explores the critical relationship between software-...

Self-adaptive systems (SASs) can change their structures autonomously and dynamically adapt their behaviors aiming at (i) attaining longer-term system goals and (ii) coping with inevitable dynamics and changes in their operational environments that are ...

Increasingly, artificial intelligence (AI) is being used to support automotive systems, including autonomous vehicles (AVs) with self-driving capabilities. The premise is that learning-enabled systems (LESs), those systems that have one or more AI ...