Modeling Context and Dynamic Adaptations
                                          with Feature Models
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Modeling Context and Dynamic Adaptations with Feature Models

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Self-adaptive and dynamic systems adapt their behavior according to the context of execution. The contextual information exhibits multiple variability factors which induce many possible configurations of the software system at runtime. The challenge is to specify the adaptation rules that can link the dynamic variability of the context with the possible variants of the system. Our work investigates the systematic use of feature models for modeling the context and the software variants, together with their inter relations, as a way to configure the adaptive system with respect to a particular context. A case study in the domain of video surveillance systems is used to illustrate the approach.

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Modeling Context and Dynamic Adaptations with Feature Models

  1. 1. Modeling Context and Dynamic Adaptations with Feature Models 1 1 2 1 3 3 Mathieu Acher , Philippe Collet , Franck Fleurey , Philippe Lahire , Sabine Moisan , and Jean-Paul Rigault 1 2 3 University of Nice Sophia Antipolis, CNRS, France SINTEF, Oslo, Norway INRIA Sophia Antipolis Mediterranée, France, {acher,collet,lahire}@i3s.unice.fr franck.fleurey@sintef.no {moisan,jpr}@sophia.inria.fr Problem Statement DSML Approach o Dynamic Adaptive Systems (DAS) are software systems o Consider DAS as a Software Product Line (SPL) which have to dynamically adapt their behavior in order From common assets, different programs of a domain can be assembled to cope with a changing environment. o Variants o Model also the context as an SPL o Constraints o Issues: o Context VariantConstraint -dependency o Large number of software configurations VariabilityModel o Rules 0..1 o Large number of contexts 0..* -context 0..* -dimension Dimension 1..1 -variant Variant -upper : Integer Variable Video Surveillance Case Study -property 0..* -lower : Integer -type 0..* Variants 0..* -property -propertyValue 0..* SPL of SPL of SPL of Frame to SPL of Task -property Frame Analysis Dependent BooleanVariable Property PropertyValue Segmentation Classification -direction : Integer 1..1 -value : Integer PropertyPriority 1..1 0..* -rule -priority : Integer -property Base -available Frame to Frame Task EnumVariable Rule ContextConstraint 0..1 Acquisition Segmentation Classification Dependent -priority 0..* -context Analysis -required 1..1 0..1 Revisiting the Approach with Feature Models Modeling Software Variants Configurations@run.time And-Group Xor-Group VSSystem initial context initial system Optional VSContext VSSystem Or-Group Mandatory Scene Segmentation Segmentation Classification LightingAnalyses TraversalAlgorithm LightingConditions Acquisition threshold: integer GridStep NaturalLight WithMask ArtificialLight Contour Density Model HeadlightDetect WithWindow TraversalAlgorithm KernelFunction Outdoors KernelFunction Indoors Edge TimeOfDay 1 Region Night ShadowElimination WithWindow GridStep Color Grey Edge Region Ellipse Parallelepiped Omega Classification Day Density GridStep or WithWindow excludes Edge (C1) LightingNoise Contour GridStep excludes Ellipse (C2) Flashes LightingAnalysis Edge excludes Density (C3) 2 Shadows DetectRapidChanges HeadLight Modeling Context HeadLightDetection new context SPL after reconfiguration VSContext VSContext VSSystem Scene Segmentation Scene ObjectOfInterest LightingConditions TraversalAlgorithm GridStep Camera NaturalLight WithMask LightingConditions ArtificialLight WithWindow Resolution DepthOfField Outdoors KernelFunction Sort NaturalLight Indoors Edge TimeOfDay 3 Region ArtificialLight Outdoors Indoors Vehicle Person Night ShadowElimination Classification LightingNoise TimeOfDay Day Density {flashes,headlight,shadows}: enum {night, day}: enum LightingNoise Contour Flashes LightingAnalysis Modeling Adaptation Shadows DetectRapidChanges HeadLight HeadLightDetection Night and HeadLight implies HeadLightDetection (AR0) not LightingNoise implies Region (AR1) 1 Initial deployment: configuration of the system from the context LightingNoise implies Edge (AR2) ArtificialLight implies DetectRapidChanges (AR3) 2 Dynamic update of the context happens Flashes or HeadLight implies Contour (AR4) 3 Dynamic reconfiguration of the system from the updated context Results Future Work o The concept of configuration is naturally present and defined by the o Leverage the expressiveness of FMs (e.g. attributes). semantics of FM. o Achieve an automatic translation between DSML and FMs. o Uniform representation of the context model and the software system makes possible to express relations between the two models. o Update automatically contextual information. o DSML and FM-based approaches can complement each other. o Connect state-of-the-art adaption engines to our models.

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