Towards History-Aware Self-Adaptation with Explanation Capabilities
(2019)
Presentation / Conference Contribution
García-Domínguez, A., Bencomo, N., Ullauri, J. M. P., & Paucar, L. H. G. (2019). Towards History-Aware Self-Adaptation with Explanation Capabilities. . https://doi.org/10.1109/fas-w.2019.00018
Outputs (9)
Preface to 9th International Workshop on Model-Driven Requirements Engineering (2019)
Presentation / Conference Contribution
Bencomo, N., Mussbacher, G., Moreira, A., Araújo, J., & Sánchez, P. (2019). Preface to 9th International Workshop on Model-Driven Requirements Engineering. . https://doi.org/10.1109/rew.2019.00009
Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes (2019)
Presentation / Conference Contribution
Paucar, L. H. G., & Bencomo, N. (2019). Knowledge Base K Models to Support Trade-Offs for Self-Adaptation using Markov Processes. . https://doi.org/10.1109/saso.2019.00011
Querying and Annotating Model Histories with Time-Aware Patterns (2019)
Presentation / Conference Contribution
García-Domínguez, A., Bencomo, N., Ullauri, J. M. P., & Paucar, L. H. G. (2019). Querying and Annotating Model Histories with Time-Aware Patterns. In M. Kessentini, T. Yue, A. Pretschner, S. Voss, & L. Burgueño (Eds.), . https://doi.org/10.1109/models.2019.000-2
ARRoW: automatic runtime reappraisal of weights for self-adaptation (2019)
Presentation / Conference Contribution
Paucar, L. H. G., Bencomo, N., & Yuen, K. K. F. (2019). ARRoW: automatic runtime reappraisal of weights for self-adaptation. In C. Hung, & G. A. Papadopoulos (Eds.), . https://doi.org/10.1145/3297280.3299743
An architectural framework for quality-driven adaptive continuous experimentation (2019)
Presentation / Conference Contribution
Jiménez, M. A., Rivera, L. F., Villegas, N. M., Tamura, G., Müller, H. A., & Bencomo, N. (2019). An architectural framework for quality-driven adaptive continuous experimentation. . https://doi.org/10.1109/rcose/ddree.2019.00012
Models@run.time: a guided tour of the state of the art and research challenges (2019)
Journal Article
Bencomo, N., Götz, S., & Song, H. (2019). Models@run.time: a guided tour of the state of the art and research challenges. https://doi.org/10.1007/s10270-018-00712-x
Perpetual Assurances for Self-Adaptive Systems (2019)
Journal Article
Weyns, D., Bencomo, N., Calinescu, R., Cámara, J., Ghezzi, C., Grassi, V., …Tamburrelli, G. (2019). Perpetual Assurances for Self-Adaptive Systems
RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning (2019)
Presentation / Conference Contribution
Bencomo, N., & Paucar, L. H. G. (2019). RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning. In M. Kessentini, T. Yue, A. Pretschner, S. Voss, & L. Burgueño (Eds.), . https://doi.org/10.1109/models.2019.00005[Context/Motivation] A model at runtime can be defined as an abstract representation of a system, including its structure and behaviour, which exist alongside with the running system. Runtime models provide support for decision-making and reasoning b... Read More about RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning.