A Vision to identify Architectural Smells in Self-Adaptive Systems using Behavioral Maps

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Self-adaptive systems can be implemented as Dynamic Software Product Lines (DSPLs) via dynamically enabling or disabling features at runtime based on a feature model. However, the runtime (re)configuration may negatively impact the system's architectural qualities, exhibiting architectural bad smells. Such smells may appear in only very specific runtime conditions, and the combinatorial explosion of the number of configurations induced by features makes exhaustive analysis intractable. We are therefore targeting smell detection at runtime for one specific configuration determined through a MAPE-K loop. To support smell detection, we propose the Behavioral Map (BM) formalism to derive automatically key architectural characteristics from different sources (feature model, source code, and other deployment artifacts) and represent them in a graph. We provide identification guidelines based on the BM for four architectural smells and illustrate the approach on Smart Home Environment (SHE) DSPL.
langue originaleAnglais
titreECSA2021 Companion Volume
Sous-titre4th Context-aware, Autonomous and Smart Architectures International Workshop (CASA)
rédacteurs en chefRobert Heinrich, Raffaela Mirandola, Danny Weyns
Lieu de publicationVäxjö, Sweden
EditeurCEUR Workshop Proceedings
Nombre de pages6
Etat de la publicationPublié - 13 sept. 2021
Evénement15th European Conference on Software Architecture (ECSA 2021) - Växjö, Suède
Durée: 13 sept. 202117 sept. 2021

Une conférence

Une conférence15th European Conference on Software Architecture (ECSA 2021)
Titre abrégéECSA
La villeVäxjö
Adresse Internet

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