Weyns, Danny;
Calinescu, Radu;
Mirandola, Raffaela;
Tei, Kenji;
Acosta, Maribel;
Bennaceur, Amel;
Boltz, Nicolas;
... Zisman, Andrea; + view all
(2023)
Towards a Research Agenda for Understanding and ManagingUncertainty in Self-Adaptive Systems.
ACM SIGSOFT Software Engineering Notes
, 48
, Article 4. 10.1145/3617946.3617951.
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Abstract
Despite considerable research efforts on handling uncertainty in self-adaptive systems, a comprehensive understanding of the precise nature of uncertainty is still lacking. This paper summarises the findings of the 2023 Bertinoro Seminar on Uncertainty in Self- Adaptive Systems, which aimed at thoroughly investigating the notion of uncertainty, and outlining open challenges associated with its handling in self-adaptive systems. The seminar discussions were centered around five core topics: (1) agile end-toend handling of uncertainties in goal-oriented self-adaptive systems, (2) managing uncertainty risks for self-adaptive systems, (3) uncertainty propagation and interaction, (4) uncertainty in self-adaptive machine learning systems, and (5) human empowerment under uncertainty. Building on the insights from these discussions, we propose a research agenda listing key open challenges, and a possible way forward for addressing them in the coming years.
| Type: | Article |
|---|---|
| Title: | Towards a Research Agenda for Understanding and ManagingUncertainty in Self-Adaptive Systems |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1145/3617946.3617951 |
| Publisher version: | https://doi.org/10.1145/3617946.3617951 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery-pp.ucl.ac.uk/id/eprint/10183721 |
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