SEAMS 2019
Sat 25 - Sun 26 May 2019 Montreal, QC, Canada
co-located with ICSE 2019
Sat 25 May 2019 16:25 - 16:50 at Duluth - Requirements Chair(s): Betty H.C. Cheng

Goals are first-class entities in a self-adaptive system (SAS) as they guide the self-adaptation. A SAS often operates in dynamic and partially unknown environments, which cause uncertainty that the SAS has to address to achieve its goals. Moreover, besides the environment, other classes of uncertainty have been identified. However, these various classes and their sources are not systematically addressed by current approaches throughout the life cycle of the SAS. In general, uncertainty typically makes the assurance provision of SAS goals exclusively at design time not viable. This calls for an assurance process that spans the whole life cycle of the SAS. In this work, we propose a goal-oriented assurance process that supports taming different sources (within different classes) of uncertainty from defining the goals at design time to performing self-adaptation at runtime. Based on a goal model augmented with uncertainty annotations, we automatically generate parametric symbolic formulae with parameterized uncertainties at design time using symbolic model checking. These formulae and the goal model guide the synthesis of adaptation policies by engineers. At runtime, the generated formulae are evaluated to resolve the uncertainty and to steer the self-adaptation using the policies. In this paper, we focus on reliability and cost properties, for which we evaluate our approach on the Body Sensor Network (BSN) implemented in OpenDaVINCI. The results of the validation are promising and show that our approach is able to systematically tame multiple classes of uncertainty and that it is effective and efficient in providing assurances for the goals of self-adaptive systems.

Sat 25 May

Displayed time zone: Eastern Time (US & Canada) change

16:00 - 17:30
RequirementsSEAMS 2019 at Duluth
Chair(s): Betty H.C. Cheng Michigan State University
16:00
25m
Talk
Won't Take No for an Answer: Resource-driven Requirements AdaptationLong Paper
SEAMS 2019
Amel Bennaceur The Open University, Andrea Zisman The Open University, Ciaran Mccormick The Open University, Danny Barthaud The Open University, Bashar Nuseibeh The Open University (UK) & Lero (Ireland)
16:25
25m
Talk
Taming Uncertainty in the Assurance Process of Self-Adaptive Systems: a Goal-Oriented ApproachArtifactLong PaperFunctional
SEAMS 2019
Gabriela Félix Solano University of Brasília, Ricardo Caldas University of Brası́lia, Genaína Nunes Rodrigues University of Brasília, Thomas Vogel Humboldt-Universität zu Berlin, Patrizio Pelliccione Chalmers | University of Gothenburg and University of L'Aquila
Pre-print
16:50
5m
Talk
PiStarGODA-MDP: A Goal-Oriented Framework to Support Assurances ProvisionArtifactFunctional
SEAMS 2019
Gabriela Félix Solano University of Brasília, Ricardo Caldas University of Brası́lia, Genaina Rodrigues University of Brasilia, Thomas Vogel Humboldt-Universität zu Berlin, Patrizio Pelliccione Chalmers | University of Gothenburg and University of L'Aquila
16:55
15m
Talk
Inferring Analyzable Models from Trajectories of Spatially-Distributed Internet-of-ThingsArtifactFunctional
SEAMS 2019
Christos Tsigkanos Technische Universität Wien, Laura Nenzi University of Trieste, Michele Loreti University of Camerino, Martin Garriga , Schahram Dustdar TU Wien, Carlo Ghezzi Politecnico di Milano
17:10
15m
Talk
Dragonfly: a Tool for Simulating Self-Adaptive Drone BehavioursArtifactReusable
SEAMS 2019
Paulo Maia State University of Ceará, Lucas Vieira State University of Ceará, Matheus Chagas State University of Ceará, Yijun Yu The Open University, UK, Andrea Zisman The Open University, Bashar Nuseibeh The Open University (UK) & Lero (Ireland)