Analysing Self-Adaptive Systems as Software Product Lines

Self-adaptation is a crucial feature of autonomous systems that must cope with uncertainties in, e.g., their environment and their internal state. Self-adaptive systems (SASs) can be realised as two-layered systems, introducing a separation of concerns between the domain-specific functionalities of the system (the managed subsystem) and the adaptation logic (the managing subsystem), i.e., introducing an external feedback loop for managing adaptation in the system. We present an approach to model SASs as dynamic software product lines (SPLs) and leverage existing approaches to SPL-based analysis for the analysis of SASs. To do so, the functionalities of the SAS are modelled in a feature model, capturing the SAS's variability. This allows us to model the managed subsystem of the SAS as a family of systems, where each family member corresponds to a valid feature configuration of the SAS. Thus, the managed subsystem of an SAS is modelled as an SPL model; more precisely, a probabilistic featured transition system. The managing subsystem of an SAS is modelled as a control layer capable of dynamically switching between these valid configurations, depending on both environmental and internal conditions. We demonstrate the approach on a small-scale evaluation of a self-adaptive autonomous underwater vehicle used for pipeline inspection, which we model and analyse with the feature-aware probabilistic model checker ProFeat. The approach allows us to analyse probabilistic reward and safety properties for the SAS, as well as the correctness of its adaptation logic.