Goal and Feature Model Optimization for the Design and Self-Adaptation of Socio-Cyber-Physical Systems

Abstract

Socio-cyber-physical systems (SCPSs) are cyber-physical systems with social concerns. Many emerging SCPSs, often qualified as “smart”, need such concerns to be addressed not only at design time but also at runtime, often by adapting dynamically to surrounding contexts, to keep providing optimal value to users. A comprehensive requirements and design modeling approach is needed to incorporate social concerns (e.g., using goal modeling) into SCPS development activities. This paper introduces an optimization method that provides design-time and runtime solutions for self-adaptive SCPSs while supporting the validation of their design models. The method helps satisfying the goals of the SCPS and its stakeholders by monitoring the system’s environment and qualities, while enforcing correctness constraints specified in a feature model. We integrate arithmetic functions generated automatically from goal and feature models to build a combined goal-feature model and synchronize the values of the features shared between i) the objective function represented by goal functions, and ii) the constraints represented by feature functions. The goal-feature model is solved by an optimization tool (IBM CPLEX) in order to calculate optimal adaptation solutions for common situations at design time. Runtime optimization is also used by the system for adapting to situations unanticipated during design. We use a Smart Home Management System case study to assess how well the method can be used to manage selection among alternatives according to monitored environmental conditions while solving emergent conflicts. Further experiments on the use of the method for runtime adaptation show good performance for realistic models and good scalability overall. Some remaining challenges and limitations exist, including the availability of quantitative models as inputs.