Industrial elevator systems are complex Cyber-Physical Systems operating in uncertain environments and experiencing uncertain passenger behaviors, hardware delays, and software errors. Identifying, understanding, and classifying such uncertainties are essential
to enable system designers to reason about uncertainties and subsequently develop solutions for empowering elevator systems to deal with uncertainties systematically. To this end, we present a method, called RuCynefin, based on the Cynefin framework to classify uncertainties in industrial elevator systems from our industrial partner (Orona, Spain), results of which can then be used for assessing their robustness. RuCynefin is equipped with a novel classification algorithm to identify the Cynefin contexts for a variety of uncertainties in industrial elevator systems, and a novel metric for measuring the robustness using the uncertainty classification. We evaluated RuCynefin with an industrial case study of 90 dispatchers from Orona to assess their robustness against uncertainties. Results show that RuCynefin could effectively identify several situations for which certain dispatchers were not robust. Specifically, 93% of such versions showed some degree of low robustness against
uncertainties. We also provide insights on the potential practical usages of RuCynefin, which are useful for practitioners in this field.
Authors: Liping Han, Tao Yue, Shaukat Ali, Aitor Arrieta and Maite Arratibel
Title of the source: ESEC/FSE 2022: Proceedings of the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering
Publisher: Association for Computing Machinery