Towards model-based derivation of systems in the industrial automation domain

Many systems in the industrial automation domain include information systems. They manage manufacturing processes and control numerous distributed hardware and software components. In current practice, the development and reuse of such systems is costly and time-consuming, due to the variability of systems' topology and processes. Up to now, product line approaches for systematic modeling and management of variability have not been well established for such complex domains. In this paper, we present a model-based approach to support the derivation of systems in the target domain. The proposed architecture of the derivation infrastructure enables feature-, topology- and process configuration to be integrated into the multi-staged derivation process. We have developed a prototype to prove feasibility and improvement of derivation efficiency. We report the evaluation results that we collected through semi-structured interviews from domain stakeholders. The results show high potential to improve derivation efficiency by adopting the approach in practice. Finally, we report the lessons learned that raise the opportunities and challenges for future research.