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A systematic mapping of semi-formal and formal methods in requirements engineering of industrial Cyber-Physical systems

Author

Listed:
  • Farzana Zahid

    (Auckland University of Technology)

  • Awais Tanveer

    (Auckland University of Technology)

  • Matthew M. Y. Kuo

    (Auckland University of Technology)

  • Roopak Sinha

    (Auckland University of Technology)

Abstract

The requirements engineering of Industrial Cyber-Physical Systems is extremely challenging due to large system sizes, component heterogeneity, involvement of multi-discipline stakeholders and machines, and continuous evolution. Formal and semi-formal languages, techniques, tools and frameworks can assist by providing repeatable and rigorous structures for eliciting, specifying, analysing, verifying and maintaining requirements. Various approaches have been proposed, but a contemporary and comprehensive study providing a landscape of the state-of-the-art is currently missing. This article reports a systematic mapping study covering 93 primary studies published between 2009 and October 2020. We categorise surveyed studies by current research directions in the use of semi-formal and formal methods for Requirements Engineering phases for Industrial Cyber-Physical Systems. We also identify gaps in current research and develop a novel conceptual model capturing the relationship between available formalisms and Requirements Engineering activities. We find that extensive work has been carried out on the formal analysis and verification of safety and timings requirements. However, the use of semi-formal notations, works on key phases like requirements elicitation and management, and the adoption of industrial standards are largely missing. Moreover, we find no literature providing methods to handle privacy and trust requirements, which have become critical concerns in this area.

Suggested Citation

  • Farzana Zahid & Awais Tanveer & Matthew M. Y. Kuo & Roopak Sinha, 2022. "A systematic mapping of semi-formal and formal methods in requirements engineering of industrial Cyber-Physical systems," Journal of Intelligent Manufacturing, Springer, vol. 33(6), pages 1603-1638, August.
    • Handle: RePEc:spr:joinma:v:33:y:2022:i:6:d:10.1007_s10845-021-01753-8
    DOI: 10.1007/s10845-021-01753-8
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    References listed on IDEAS

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    1. Ercan Oztemel & Samet Gursev, 2020. "Literature review of Industry 4.0 and related technologies," Journal of Intelligent Manufacturing, Springer, vol. 31(1), pages 127-182, January.
    2. Byeongwoo Jeon & Joo-Sung Yoon & Jumyung Um & Suk-Hwan Suh, 2020. "The architecture development of Industry 4.0 compliant smart machine tool system (SMTS)," Journal of Intelligent Manufacturing, Springer, vol. 31(8), pages 1837-1859, December.
    3. Franceschini, Fiorenzo & Maisano, Domenico & Mastrogiacomo, Luca, 2016. "Empirical analysis and classification of database errors in Scopus and Web of Science," Journal of Informetrics, Elsevier, vol. 10(4), pages 933-953.
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