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
All Outputs (51)
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
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
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
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.
Software Engineering for Self-Adaptive Systems: Research Challenges in the Provision of Assurances (2013)
Presentation / Conference Contribution
Lemos, R. D., Garlan, D., Ghezzi, C., Giese, H., Andersson, J., Litoiu, M., …Zambonelli, F. (2013). Software Engineering for Self-Adaptive Systems: Research Challenges in the Provision of Assurances. In R. de Lemos, D. Garlan, C. Ghezzi, & H. Giese (Eds.), . https://doi.org/10.1007/978-3-319-74183-3_1The important concern for modern software systems is to become more cost-effective, while being versatile, flexible, resilient, dependable, energy-efficient, customisable, configurable and self-optimising when reacting to run-time changes that may oc... Read More about Software Engineering for Self-Adaptive Systems: Research Challenges in the Provision of Assurances.
Addressing the QoS drift in specification models of self-adaptive service-based systems (2013)
Presentation / Conference Contribution
Torres, R., Bencomo, N., & Astudillo, H. (2013). Addressing the QoS drift in specification models of self-adaptive service-based systems. . https://doi.org/10.1109/raise.2013.6615201
Supporting Decision-Making for Self-Adaptive Systems: From Goal Models to Dynamic Decision Networks (2013)
Presentation / Conference Contribution
Bencomo, N., & Belaggoun, A. (2013). Supporting Decision-Making for Self-Adaptive Systems: From Goal Models to Dynamic Decision Networks. In J. Dörr, & A. L. Opdahl (Eds.), . https://doi.org/10.1007/978-3-642-37422-7_16[Context/Motivation] Different modeling techniques have been used to model requirements and decision-making of self-adaptive systems (SASs). Specifically, goal models have been prolific in supporting decision-making depending on partial and total ful... Read More about Supporting Decision-Making for Self-Adaptive Systems: From Goal Models to Dynamic Decision Networks.
Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks (2013)
Presentation / Conference Contribution
Bencomo, N., Belaggoun, A., & Issarny, V. (2013). Bayesian artificial intelligence for tackling uncertainty in self-adaptive systems: The case of dynamic decision networks. . https://doi.org/10.1109/raise.2013.6615198
Dynamic decision networks for decision-making in self-adaptive systems: a case study (2013)
Presentation / Conference Contribution
Bencomo, N., Belaggoun, A., & Issarny, V. (2013). Dynamic decision networks for decision-making in self-adaptive systems: a case study. In M. Litoiu, & J. Mylopoulos (Eds.), . https://doi.org/10.1109/seams.2013.6595498Bayesian decision theory is increasingly applied to support decision-making processes under environmental variability and uncertainty. Researchers from application areas like psychology and biomedicine have applied these techniques successfully. Howe... Read More about Dynamic decision networks for decision-making in self-adaptive systems: a case study.
The role of models@run.time in supporting on-the-fly interoperability (2012)
Journal Article
Bencomo, N., Bennaceur, A., Grace, P., Blair, G., & Issarny, V. (2013). The role of models@run.time in supporting on-the-fly interoperability. Computing, 95(3), 167-190. https://doi.org/10.1007/s00607-012-0224-x