一句话：提出RuCynefin方法分类工业电梯系统中的不确定性，并基于分类度量调度器鲁棒性。
工业电梯系统是运行在不确定环境中的复杂信息物理系统，面临乘客行为、硬件延迟和软件错误等不确定性。识别并分类这些不确定性，是设计者推理并系统化提升鲁棒性的前提。作者提出基于Cynefin框架的RuCynefin方法，用于分类Orona电梯系统中的不确定性并评估鲁棒性。该方法包含新的分类算法，可为多种不确定性确定Cynefin语境，并提出利用分类结果度量鲁棒性的新指标。作者以Orona的90个调度器为案例，评估其面对不确定性时的鲁棒性。结果表明，RuCynefin能有效识别若干调度器不鲁棒的情形，其中93%的此类版本表现出某种程度的低鲁棒性。文章还总结了方法的潜在实践用途。
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.
DOI 原文 ·
@inproceedings{paperbot6990,
title = {Are elevator software robust against uncertainties? results and experiences from an industrial case study},
author = {Liping Han and Tao Yue and Shaukat Ali and Aitor Arrieta and Maite Arratibel},
booktitle = {FSE},
year = {2022},
doi = {10.1145/3540250.3558955}
}