Related papers: Self-Adaptation in Industry: A Survey
In order to increase productivity, capability, and data exploitation, numerous defense applications are experiencing an integration of state-of-the-art machine learning and AI into their architectures. Especially for defense applications,…
Adaptive and flexible production systems require modular and reusable software especially considering their long term life cycle of up to 50 years. SWMAT4aPS, an approach to measure Software Maturity for automated Production Systems is…
In the era of digital transformation, new technological foundations and possibilities for collaboration, production as well as organization open up many opportunities to work differently in the future. The digitization of workflows results…
A self-adaptive system can modify its own structure and behavior at runtime based on its perception of the environment, of itself and of its requirements. To develop a self-adaptive system, software developers codify knowledge about the…
While Sustainable Software Engineering (SSE) tools are widely studied in academia, their practical feasibility in industrial workflows, particularly in regulated environments, remains poorly understood. This study investigates how software…
Monitoring software systems at runtime is key for understanding workloads, debugging, and self-adaptation. It typically involves collecting and storing observable software data, which can be analyzed online or offline. Despite the…
Context: While there are many success stories of achieving high reuse and improved quality using software platforms, there is a need to investigate the issues and challenges organizations face when transitioning to a software platform…
User feedback has grown in importance for organizations to improve software products. Prior studies focused primarily on feedback collection and reported a high-level overview of the processes, often overlooking how practitioners reason…
Context: The software industry needs to adapt itself to a rapidly changing market. Continuous practices (Continuous Integration, Continuous Delivery and Continuous Deployment), commonly found in Agile development processes, it is possible…
From formal and practical analysis, we identify new challenges that self-adaptive systems pose to the process of quality assurance. When tackling these, the effort spent on various tasks in the process of software engineering is naturally…
Many organizations adopt DevOps practices and tools in order to break down silos within the organization, improve software quality and delivery, and increase customer satisfaction. However, the impact of the individual practices on the…
AI-empowered tools have emerged as a transformative force, fundamentally reshaping the software development industry and promising far-reaching impacts across diverse sectors. This study investigates the adoption, impact, and security…
Context: The software development industry is rapidly adopting machine learning for transitioning modern day software systems towards highly intelligent and self-learning systems. However, the full potential of machine learning for…
A race is underway to embed advanced AI capabilities into products. These product copilots enable users to ask questions in natural language and receive relevant responses that are specific to the user's context. In fact, virtually every…
Adaptive control provides techniques for adjusting control parameters in real time to maintain system performance despite unknown or changing process parameters. These methods use real data to tune controllers and adjust plant models or…
A self-learning adaptive system (SLAS) uses machine learning to enable and enhance its adaptability. Such systems are expected to perform well in dynamic situations. For learning high-performance adaptation policy, some assumptions must be…
Collective adaptive systems are an emerging class of networked computational systems, particularly suited in application domains such as smart cities, complex sensor networks, and the Internet of Things. These systems tend to feature large…
Cloud computing is gaining more and more traction as a deployment and provisioning model for software. While a large body of research already covers how to optimally operate a cloud system, we still lack insights into how professional…
The demand for rapid software delivery in the Information Technology (IT) industry has significantly intensified, emphasising the need for faster software products and service releases with enhanced features to meet customer expectations.…
Given the ever-increasing complexity of adaptable software systems and their commonly hidden internal information (e.g., software runs in the public cloud), machine learning based performance modeling has gained momentum for evaluating,…