Digital Twins (DTs) are revolutionizing Cyber-Physical Systems (CPSs) in many ways, including their development and operation. The significant interest of industry and academia in DTs has led to various definitions of DTs and related concepts, as seen in many recently published papers. Thus, there is a need for precisely defining different DT concepts and their relationships. To this end, we present a conceptual model that captures various DT concepts and their relationships, some of which are from the published literature, to provide a unified understanding of these concepts in the context of CPSs. The conceptual model is implemented as a set of Unified Modeling Language (UML) class diagrams and the concepts in the conceptual model are explained with a running example of an automated warehouse case study from published literature and based on the authors’ experience of working with the real CPS case study in previous projects.

Authors: Tao Yue, Paolo Arcaini and Shaukat Ali

Title of the source: 2021 10th International Symposium On Leveraging Applications of Formal Methods, Verification and Validation (ISoLA)

Publisher:  Springer

Relevant pages:  54-71

Year: 2021

Elevators, as other cyber-physical systems, need to deal with uncertainty during their operation due to several factors such as passengers and hardware. Such uncertainties could affect the quality of service promised by elevators and in the worst case lead to safety hazards. Thus, it is important that elevators are extensively tested by considering uncertainty during their development to ensure their safety in operation. To this end, we present an uncertainty testing methodology supported with a tool to test industrial dispatching systems at the Software-in-the-Loop (SiL) test level. In particular, we focus on uncertainties in passenger data and employ a Genetic Algorithm (GA) with specifically designed genetic operators to significantly reduce the quality of service of elevators, thus aiming to find uncertain situations that are difficult to extract by users. An initial experiment with an industrial dispatcher revealed that the GA significantly decreased the quality of service as compared to not considering uncertainties. The results can be used to further improve the implementation of dispatching algorithms to handle various uncertainties.

DOI: 10.1109/ISSREW53611.2021.00101

Authors:
Joritz Galarraga, Aitor Arrieta Marcos, Shaukat Ali, Goiuria Sagardui, Maite Arratibel

Title of the source: 2021 IEEE International Symposium on Software Reliability Engineering Workshops (ISSREW)

Publisher:  IEEE

Relevant pages:  353-358

Year: 2021

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The software of systems of elevators needs constant maintenance to deal with new functionality, bug fixes or legislation changes. To automatically validate the software of these systems, a typical approach in industry is to use regression oracles, which execute test inputs both in the software version under test and in a previous software version. However, these practices require a long test execution time and cannot be re-used at different test phases. To deal with these issues, we propose DARIO, a test oracle that relies on regression machine-learning algorithms to detect both functional and non-functional problems of the system. The machine-learning algorithms of this oracle are trained by using data from previously tested versions to predict reference functional and non-functional performance
values of the new versions. An empirical evaluation with an industrial case study demonstrates the feasibility of using our approach. A total of five regression learning algorithms were validated by using mutation testing techniques. For the context of functional bugs, the accuracy when predicting verdicts by DARIO ranged between 95% to 98%, across the different scenarios proposed. For the context of non-functional bugs, were competitive too, having an accuracy when predicting verdicts by DARIO ranging between 83% to 87%.

DOI: https://doi.org/10.1002/smr.2465

Authors:
Aitor Gartziandia, Aitor Arrieta, Jon Ayerdi, Miren Illarramendi, Aitor Agirre, Goiuria Sagardui, Maite Arratibel

Title of the source: Journal of Software: Evolution and Process

Publisher: 

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Year: 2022

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Abstract—The rapid increase in number of devices in Internet-of-Things generates astronomic amounts of data. Dealing with noisy and low quality data uses more effort than the data analysis itself. Dealing with noisy data at the source would significantly reduce the effort of pre-processing during analysis, as well as the storage and bandwidth overhead. In this paper we introduce an Adaptive Signal Processing Platform (ASPF) for CPS/IoT Ecosystems. It provides ability to dynamically detect noise variation in a signal and successfully filter these components out of the signal leaving only clean and useful data. The paper shows two approaches with different requirements on effort and scalability.

DOI: https://doi.org/10.1109/ICIT46573.2021.9453496

Authors:
Haris Isakovic, Stefan Dangl, Zlatan Tucakovic, Radu Grosu

Title of the source: 2021 22nd IEEE International Conference on Industrial Technology (ICIT)

Publisher: IEEE

Relevant pages: 1391-1396

Year: 2021

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Abstract—One of the major challenges in the verification of complex industrial Cyber-Physical Systems is the difficulty of determining whether a particular system output or behaviour is correct or not, the socalled test oracle problem. Metamorphic testing alleviates the oracle problem by reasoning on the relations that are expected to hold among multiple executions of the system under test, which are known as Metamorphic Relations (MRs). However, the development of effective MRs is often challenging and requires the involvement of domain experts. In this paper, we present a case study aiming at automating this process. To this end, we implemented GAssertMRs, a tool to automatically generate MRs with genetic programming. We assess the cost-effectiveness of this tool in the context of an industrial case study from the elevation domain. Our experimental results show that in most cases GAssertMRs outperforms the other baselines, including manually generated MRs developed with the help of domain experts. We then describe the lessons learned from our experiments and we outline the future work for the adoption of this technique by industrial practitioners.

DOI: https://doi.org/10.1145/3468264.3473920

Authors:
Jon Ayerdi, Valerio Terragni, Aitor Arrieta, Paolo Tonella, Goiuria Sagardui, Maite Arratibel

Title of the source: Proceedings of the 29th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE ’21)

Publisher: ACM

Relevant pages: 1264-1274

Year: 2021

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Abstract—Elevators are among the oldest and most widespread transportation systems, yet their complexity increases rapidly to satisfy customization demands and to meet quality of service requirements. Verification and validation tasks in this context are costly, since they rely on the manual intervention of domain experts at some points of the process. This is mainly due to the difficulty to assess whether the elevators behave as expected in the different test scenarios, the so-called test oracle problem. Metamorphic testing is a thriving testing technique that alleviates the oracle problem by reasoning on the relations among multiple executions of the system under test, the so-called metamorphic relations. In this practical experience paper, we report on the application of metamorphic testing to verify an industrial elevator dispatcher. Together with domain experts from the elevation sector, we defined multiple metamorphic relations that consider domain-specific quality of service measures. Evaluation results with seeded faults show that the approach is effective at detecting faults automatically.

DOI: https://doi.org/10.1109/ISSRE5003.2020.00019

Authors:
Jon Ayerdi, Sergio Segura, Aitor Arrieta, Goiuria Sagardui, Maite Arratibel

Title of the source: IEEE 31st International Symposium on Software Reliability Engineering (ISSRE)

Publisher: IEEE

Relevant pages: 104-114

Year: 2020

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