Zobrazeno 1 - 10
of 1 586
pro vyhledávání: '"Weyns, A."'
Autor:
Töpfer, Michal, Plášil, František, Bureš, Tomáš, Hnětynka, Petr, Kruliš, Martin, Weyns, Danny
Online machine learning (ML) is often used in self-adaptive systems to strengthen the adaptation mechanism and improve the system utility. Despite such benefits, applying online ML for self-adaptation can be challenging, and not many papers report it
Externí odkaz:
http://arxiv.org/abs/2309.05805
In A/B testing two variants of a piece of software are compared in the field from an end user's point of view, enabling data-driven decision making. While widely used in practice, no comprehensive study has been conducted on the state-of-the-art in A
Externí odkaz:
http://arxiv.org/abs/2308.04929
Autor:
Quin, Federico, Weyns, Danny
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 conducted sequentially, with each experiment targeting the enti
Externí odkaz:
http://arxiv.org/abs/2306.01407
Modern software systems often have to cope with uncertain operation conditions, such as changing workloads or fluctuating interference in a wireless network. To ensure that these systems meet their goals these uncertainties have to be mitigated. One
Externí odkaz:
http://arxiv.org/abs/2306.01404
Autor:
Weyns, Danny, Andersson, Jesper
Engineering long-running computing systems that achieve their goals under ever-changing conditions pose significant challenges. Self-adaptation has shown to be a viable approach to dealing with changing conditions. Yet, the capabilities of a self-ada
Externí odkaz:
http://arxiv.org/abs/2303.15260
Autor:
Weyns, Danny, Vogel, Thomas
Despite the vast body of knowledge developed by the self-adaptive systems community and the wide use of self-adaptation in industry, it is unclear whether or to what extent industry leverages output of academics. Hence, it is important for the resear
Externí odkaz:
http://arxiv.org/abs/2303.15025
Autor:
Weyns, Danny, Avegriou, Paris, Calinescu, Radu, Hezavehi, Sara M., Mirandola, Raffaela, Perez-Palacin, Diego
Over the past two decades, researchers and engineers have extensively studied the problem of how to enable a software system to deal with uncertain operating conditions. One prominent solution to this problem is self-adaptation, which equips a softwa
Externí odkaz:
http://arxiv.org/abs/2211.17218
Autor:
Weyns, Danny, Gerostathopoulos, Ilias, Abbas, Nadeem, Andersson, Jesper, Biffl, Stefan, Brada, Premek, Bures, Tomas, Di Salle, Amleto, Galster, Matthias, Lago, Patricia, Lewis, Grace, Litoiu, Marin, Musil, Angelika, Musil, Juergen, Patros, Panos, Pelliccione, Patrizio
Computing systems form the backbone of many areas in our society, from manufacturing to traffic control, healthcare, and financial systems. When software plays a vital role in the design, construction, and operation, these systems are referred as sof
Externí odkaz:
http://arxiv.org/abs/2211.03116
Autor:
Gheibi, Omid, Weyns, Danny
Recently, machine learning (ML) has become a popular approach to support self-adaptation. ML has been used to deal with several problems in self-adaptation, such as maintaining an up-to-date runtime model under uncertainty and scalable decision-makin
Externí odkaz:
http://arxiv.org/abs/2211.02658
Autor:
Weyns, Danny, Gerostathopoulos, Ilias, Buhnova, Barbora, Cardozo, Nicolas, Cioroaica, Emilia, Dusparic, Ivana, Grunske, Lars, Jamshidi, Pooyan, Julien, Christine, Michael, Judith, Moreno, Gabriel, Nejati, Shiva, Pelliccione, Patrizio, Quin, Federico, Rodrigues, Genaina, Schmerl, Bradley, Vieira, Marco, Vogel, Thomas, Wohlrab, Rebekka
Artifacts support evaluating new research results and help comparing them with the state of the art in a field of interest. Over the past years, several artifacts have been introduced to support research in the field of self-adaptive systems. While t
Externí odkaz:
http://arxiv.org/abs/2206.12492