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ORCID15th SEAMS@ICSE 2020: Seoul, Koreaa
- Shinichi Honiden, Elisabetta Di Nitto, Radu Calinescu:
SEAMS '20: IEEE/ACM 15th International Symposium on Software Engineering for Adaptive and Self-Managing Systems, Seoul, Republic of Korea, 29 June - 3 July, 2020. ACM 2020, ISBN 978-1-4503-7962-5 - A. E. Eiben:
Evolving robot software and hardware. 1-4 - Corina S. Pasareanu, Hayes Converse, Antonio Filieri, Divya Gopinath:
On the probabilistic analysis of neural networks. 5-8 - Ricardo Diniz Caldas
, Arthur Rodrigues, Eric Bernd Gil, Genaína Nunes Rodrigues, Thomas Vogel, Patrizio Pelliccione:
A hybrid approach combining control theory and AI for engineering self-adaptive systems. 9-19 - M. Jeroen Van Der Donckt, Danny Weyns, Federico Quin, Jonas Van Der Donckt, Sam Michiels:
Applying deep learning to reduce large adaptation spaces of self-adaptive systems with multiple types of goals. 20-30 - Max Scheerer, Jonas Klamroth, Ralf Reussner, Bernhard Beckert:
Towards classes of architectural dependability assurance for machine-learning-based systems. 31-37 - Kenji Mori, Naoko Okubo, Yasushi Ueda, Masafumi Katahira, Toshiyuki Amagasa:
Supporting viewpoints to review the lack of requirements in space systems with machine learning. 38-44 - Joachim Hänsel, Christian M. Adriano, Johannes Dyck, Holger Giese:
Collective risk minimization via a bayesian model for statistical software testing. 45-56 - Kun Liu, Xiaoyi Zhang, Paolo Arcaini, Fuyuki Ishikawa, Wenpin Jiao:
Leveraging test logs for building a self-adaptive path planner. 57-63 - Yuning He, Johann Schumann:
A framework for the analysis of adaptive systems using bayesian statistics. 64-70 - José D'Abruzzo Pereira, Rui Silva, Nuno Antunes, Jorge Luiz Machado da Silva, Breno de França, Regina Moraes, Marco Vieira:
A platform to enable self-adaptive cloud applications using trustworthiness properties. 71-77 - Javier Cámara, Alessandro Vittorio Papadopoulos, Thomas Vogel, Danny Weyns, David Garlan, Shihong Huang, Kenji Tei:
Towards bridging the gap between control and self-adaptive system properties. 78-84 - Abdessalam Elhabbash, Vatsala Nundloll, Yehia Elkhatib, Gordon S. Blair, Vicent Sanz Marco:
An ontological architecture for principled and automated system of systems composition. 85-95 - Satish Kumar, Tao Chen, Rami Bahsoon, Rajkumar Buyya:
DATESSO: self-adapting service composition with debt-aware two levels constraint reasoning. 96-107 - Nicolás Cardozo, Ivana Dusparic:
Learning run-time compositions of interacting adaptations. 108-114 - Ilja Shmelkin:
Monitoring for control in role-oriented self-adaptive systems. 115-119 - Tim Kluge:
A role-based architecture for self-adaptive cyber-physical systems. 120-124 - Javier Cámara, Bradley R. Schmerl, David Garlan:
Software architecture and task plan co-adaptation for mobile service robots. 125-136 - Seung Yeob Shin, Shiva Nejati, Mehrdad Sabetzadeh, Lionel C. Briand, Chetan Arora, Frank Zimmer:
Dynamic adaptation of software-defined networks for IoT systems: a search-based approach. 137-148 - Ana Petrovska, Sergio Quijano, Ilias Gerostathopoulos, Alexander Pretschner:
Knowledge aggregation with subjective logic in multi-agent self-adaptive cyber-physical systems. 149-155 - Petr Hnetynka, Tomás Bures, Ilias Gerostathopoulos, Jan Pacovsky:
Using component ensembles for modeling autonomic component collaboration in smart farming. 156-162 - Danny Weyns:
Towards a code of ethics for autonomous and self-adaptive systems. 163-165 - Rogério de Lemos:
Human in the loop: what is the point of no return? 165-166 - Assaf Marron, Lior Limonad, Sarah Pollack, David Harel:
Expecting the unexpected: developing autonomous-system design principles for reacting to unpredicted events and conditions. 167-173 - Silvan Zeller, Narges Khakpour, Danny Weyns, Daniel Deogun:
Self-protection against business logic vulnerabilities. 174-180 - Nianyu Li, Sridhar Adepu, Eunsuk Kang, David Garlan:
Explanations for human-on-the-loop: a probabilistic model checking approach. 181-187 - Lucas Sakizloglou, Sona Ghahremani, Thomas Brand, Matthias Barkowsky, Holger Giese:
Towards highly scalable runtime models with history. 188-194
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