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

Abstract

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.