@article{15896,
  keywords = {Cyber-Physical Systems, Product Line Engineering, Variability Modeling, Automated configuration, Multi-stage and multi-step configuration process, Constraint classification, Real-world case studies},
  author = {Safdar Safdar and Hong Lu and Tao Yue and Shaukat Ali and Kunming Nie},
  title = {A framework for automated multi-stage and multi-step product confguration of cyber-physical systems},
  abstract = {Product line engineering (PLE) has been employed to large-scale cyber-physical systems (CPSs) to provide customization based on users{\textquoteright} needs. A PLE methodology can be characterized by its support for capturing and managing the abstractions as commonalities and variabilities and the automation of the confguration process for efective selection and customization of reusable artifacts. The automation of a confguration process heavily relies on the captured abstractions and formally specifed constraints using a well-defned modeling methodology. Based on the results of our previous work and a thorough literature review, in this paper, we propose a conceptual framework to support multi-stage and multi-step automated product confguration of CPSs, including a comprehensive classifcation of constraints and a list of automated functionalities of a CPS confguration solution. Such a framework can serve as a guide for researchers and practitioners to evaluate an existing CPS PLE solution or devise a novel CPS PLE solution. To validate the framework, we conducted three real-world case studies. Results show that the framework fulflls all the requirements of the case studies in terms of capturing and managing variabilities and constraints. Results of the literature review indicate that the framework covers all the functionalities concerned by the literature, suggesting that the framework is complete for enabling the maximum automation of confguration in CPS PLE.},
  year = {2020},
  journal = {Software and Systems Modeling (SoSym)},
  volume = {19},
  pages = {1-55},
  month = {06/2020},
  publisher = {Springer},
  doi = {https://doi.org/10.1007/s10270-020-00803-8},
}