Evaluating Variability Modeling Techniques for Supporting Cyber-Physical System Product Line Engineering

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.

[1]  Krzysztof Czarnecki,et al.  A survey of variability modeling in industrial practice , 2013, VaMoS.

[2]  Krzysztof Czarnecki,et al.  Cool features and tough decisions: a comparison of variability modeling approaches , 2012, VaMoS.

[3]  Klaus Schmid,et al.  A systematic analysis of textual variability modeling languages , 2013, SPLC '13.

[4]  Muhammad Zohaib Z. Iqbal,et al.  Empirical Evaluation of UML Modeling Tools-A Controlled Experiment , 2015, ECMFA.

[5]  Krzysztof Czarnecki,et al.  Variability modeling in the real: a perspective from the operating systems domain , 2010, ASE '10.

[6]  Edward A. Lee,et al.  Modeling Cyber–Physical Systems , 2012, Proceedings of the IEEE.

[7]  Danny Weyns,et al.  Variability in Software Systems—A Systematic Literature Review , 2014, IEEE Transactions on Software Engineering.

[8]  Rafael Capilla,et al.  Context variability modeling for runtime configuration of service-based dynamic software product lines , 2014, SPLC '14.

[9]  Li Zhang,et al.  Constraints: The Core of Supporting Automated Product Configuration of Cyber-Physical Systems , 2013, MoDELS.

[10]  Krzysztof Czarnecki,et al.  Staged Configuration Using Feature Models , 2004, SPLC.

[11]  Bran Selic,et al.  SimPL: A product-line modeling methodology for families of integrated control systems , 2013, Inf. Softw. Technol..

[12]  Bran Selic,et al.  Cyber-physical system product line engineering: comprehensive domain analysis and experience report , 2015, SPLC.

[13]  Camille Salinesi,et al.  Criteria for Comparing Requirements Variability Modeling Notations for Product Lines , 2006, Fourth International Workshop on Comparative Evaluation in Requirements Engineering (CERE'06 - RE'06 Workshop).

[14]  Lianping Chen,et al.  Variability management in software product lines: a systematic review , 2009, SPLC.

[15]  Marco Sinnema,et al.  Classifying variability modeling techniques , 2007, Inf. Softw. Technol..

[16]  Kyo Chul Kang,et al.  Feature-Oriented Domain Analysis (FODA) Feasibility Study , 1990 .

[17]  Bran Selic,et al.  Modeling and Analysis of Real-Time and Embedded Systems with UML and MARTE: Developing Cyber-Physical Systems , 2013 .

[18]  Danda B. Rawat,et al.  Cyber-Physical Systems: From Theory to Practice , 2015 .