Product line engineering (PLE) has been employed to large-scale cyber-physical systems (CPSs) to provide customization based on users’ 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.

Modern systems are being developed by integrating multiple products within/across product lines that communicate with each other through information networks. Runtime behaviors of such systems are related to product configurations and information networks. Cost-effectively supporting Product Line Engineering (PLE) of such systems is challenging mainly because of lacking the support of automation of the configuration process. Capturing rules is the key for automating the configuration process in PLE. However, there does not exist explicitly-specified rules constraining configurable parameter values of such products and product lines. Manually specifying such rules is tedious and time-consuming. To address this challenge, in this paper, we present an improved version (named as SBRM+) of our previously proposed Search-based Rule Mining (SBRM) approach. SBRM+ incorporates two machine learning algorithms (i.e., C4.5 and PART) and two multi-objective search algorithms (i.e., NSGA-II and NSGA-III), employs a clustering algorithm (i.e., k-means) for classifying rules as high or low confidence rules, which are used for defining three objectives to guide the search. To evaluate SBRM+ (i.e., SBRM+NSGA-II-C45, SBRM+NSGA-III-C45, SBRM+NSGA-II-PART, and SBRM+NSGA-III-PART), we performed two case studies (Cisco and Jitsi) and conducted three types of analyses of results: difference analysis, correlation analysis, and trend analysis. Results of the analyses show that all the SBRM+ approaches performed significantly better than two Random Search-based approaches (RBRM+-C45 and RBRM+-PART) in terms of fitness values, six quality indicators, and 17 machine learning quality measurements (MLQMs). As compared to RBRM+ approaches, SBRM+ approaches have improved the quality of rules based on MLQMs up to 27% for the Cisco case study and 28% for the Jitsi case study.

Nowadays, an increasing number of systems are being developed by integrating products (belonging to different products lines) that communicate with each other through information networks. Cost-effectively supporting Product Line Engineering (PLE) and in particular enabling automation of configuration in PLE is a challenge. Capturing rules is the key for enabling automation of configuration. Product configuration has a direct impact on runtime interactions of communicating products. Such products might be within or across product lines and there usually don’t exist explicitly specified rules constraining configurable parameter values of such products. Manually specifying such rules is tedious, time-consuming, and requires expert’s knowledge of the domain and the product lines. To address this challenge, we propose an approach named as SBRM that combines multi- objective search with machine learning to mine rules. To evaluate the proposed approach, we performed a real case study of two communicating Video Conferencing Systems belonging to two different product lines. Results show that SBRM performed significantly better than Random Search in terms of fitness values, Hyper-Volume, and machine learning quality measurements. When comparing with rules mined with real data, SBRM performed significantly better particularly in terms of Failed Precision (18%), Failed Recall (72%), and Failed F-measure (59%).

Modern society is increasingly dependent on Cyber-Physical Systems (CPSs) in diverse domains such as aerospace, energy and healthcare. Employing Product Line Engineering (PLE) in CPSs is cost-effective in terms of reducing production cost, and achieving high productivity of a CPS development process as well as higher quality of produced CPSs. To apply CPS PLE in practice, one needs to first select an appropriate variability modeling technique (VMT), with which variabilities of a CPS Product Line (PL) can be specified. In this paper, we proposed a set of basic and CPS-specific variation point (VP) types and modeling requirements for proposing CPS-specific VMTs. Based on the proposed set of VP types (basic and CPS-specific) and modeling requirements, we evaluated four VMTs: Feature Modeling, Cardinality Based Feature Modeling, Common Variability Language, and SimPL (a variability modeling technique dedicated to CPS PLE), with a real-world case study. Evaluation results show that none of the selected VMTs can capture all the basic and CPS-specific VP and meet all the modeling requirements. Therefore, there is a need to extend existing techniques or propose new ones to satisfy all the requirements.