Adeptness Whitepaper

This document provides a reference guide of the subsystems involved in the ADEPTNESS ecosystem and their associated microservices. It allows early adopters to map specific components in their infrastructure to check the viability and the added value of their adoption. It also describes the staged workflow to enable this microservices-based architecture, including the monitoring, validation, uncertainty detection, recovery and knowledge extraction of operational data, thereby providing benefits on the deployment effort, a reduction in time to recovery and a reduction of bugs.

Uncertainty-aware Robustness Assessment of Industrial Elevator Systems

Industrial elevator systems are commonly used software systems in our daily lives, which operate in uncertain environments such as unpredictable passenger traffic, uncertain passenger attributes and behaviors, and hardware delays. Understanding and assessing the robustness of such systems under various uncertainties enable system designers to reason about uncertainties, especially those leading to low system robustness, and consequently improve their designs and implementations in terms of handling uncertainties. To this end, we present a comprehensive empirical study conducted with industrial elevator systems provided by our industrial partner Orona, which focuses on assessing the robustness of a dispatcher, i.e., a software component responsible for elevators’ optimal scheduling. In total, we studied 90 industrial dispatchers in our empirical study. Based on the experience gained from the study, we derived an uncertainty-aware robustness assessment method (named UncerRobua) comprising a set of guidelines on how to conduct the robustness assessment and a newly proposed ranking algorithm, for supporting the robustness assessment of industrial elevator systems against uncertainties.

DOI: TBD

Authors: Liping Han, Shaukat Ali, Tao Yue, Aitor Arrieta and Maite Arratibel

Title of the source: ACM Transactions on Software Engineering and Methodology

Publisher:  ACM Journals

Relevant pages:  

Year: 2022

Uncertainty-Aware Transfer Learning to Evolve Digital Twins for Industrial Elevators

Digital twins are increasingly developed to support the development, operation, and maintenance of cyber-physical systems such as industrial elevators. However, industrial elevators continuously evolve due to changes in physical installations, introducing new software features, updating existing ones, and making changes due to regulations (e.g., enforcing restricted elevator capacity due to COVID-19), etc. Thus, digital twin functionalities (often built on neural network-based models) need to evolve themselves constantly to be synchronized with the industrial elevators. Such an evolution is preferred to be automated, as manual evolution is timeconsuming and error-prone. Moreover, collecting sufficient data to re-train neural network models of digital twins could be expensive or even infeasible. To this end, we propose unceRtaInty-aware tranSfer lEarning enriched Digital Twins (RISE-DT), a transfer learning based approach capable of transferring knowledge about the waiting time prediction capability of a digital twin of an industrial elevator across different scenarios. RISE-DT also leverages uncertainty quantification to further improve its effectiveness. To evaluate RISE-DT, we conducted experiments with 10 versions of an elevator dispatching software from Orona, Spain, which are deployed in a Software in the Loop (SiL) environment. Experiment results show that RISE-DT, on average, improves the Mean Squared Error by 13.131% and the utilization of uncertainty quantification further improves it by 2.71%.

DOI: https://doi.org/10.1145/3540250.3558957

Authors: Qinghua Xu, Shaukat Ali, Tao Yue 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

Relevant pages: 

Year: 2022


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Are Elevator Software Robust against Uncertainties? Results and Experiences from an Industrial Case Study

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: 10.1145/3540250.3558955

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

Relevant pages: 

Year: 2022

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Multi-Objective Metamorphic Test Case Selection: an Industrial Case Study

Metamorphic testing is a technique that has shown great potential to alleviate the test oracle problem by exploiting the relations among the inputs and outputs of different executions of a system. However, this approach requires multiple test executions. In applications like Cyber-Physical Systems (CPSs), where the test executions can be very expensive in terms of time
and resources needed, this can supose a problem. Therefore, it is paramount to optimize the test suite to reduce the costs of verifying the system. Test case selection is an optimization
technique which accomplishes this by selecting a subset of test cases while aiming to preserve the effectiveness of the original test suite as much as possible. While there are many approaches for test case selection in the existing literature, none of them has
been proposed for the metamorphic test case selection problem, where each metamorphic test case consists of a source and, at least, a follow-up test case pair.

In this work, we present an evolutionary multi-objective approach for the metamorphic test case selection problem, adapting existing multi-objective test selection techniques and proposing new evolutionary operators and objective functions. Furthermore, we evaluate our approach with a set of metamorphic tests developed for an industrial case study from the elevation domain. The results suggest that our approach outperforms both Random Search and the same metaheuristic algorithm without the new evolutionary operators we propose.

DOI: TBD

Authors: Jon Ayerdi, Aitor Arrieta, Ernest Bota Pobee and Maite Arratibel

Title of the source: IEEE 33rd International Symposium on Software Reliability Engineering

Publisher:  IEEE

Relevant pages:  

Year: 2022

Nouman successfully defended his licentiate thesis at MDU

Nouman successfully defended his licentiate thesis at MDU

Nouman successfully defended his licentiate thesis (mid-way to PhD) at MDU, being part of the Adeptness project and in close collaboration with Alstom Transportation AB, Sweden. The focus of his work is on model-based testing, especially utilizing GraphWalker, to automate test generation at Alstom. He also comprehensively analyzed the generated tests in terms of cost effectiveness, fault detection capabilities and test coverage, in comparison with combinatorial and manual testing at Alstom. The thesis can be found at: https://www.diva-portal.org/smash/record.jsf?pid=diva2%3A1690023&dswid=-6563

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Performance-Driven Metamorphic Testing of Cyber-Physical Systems

Cyber-Physical Systems (CPSs) are a new generation of systems which integrate software with physical processes. The increasing complexity of these systems, combined with the uncertainty in their interactions with the physical world, makes the definition of effective test oracles especially challenging, facing the well known test oracle problem. Metamorphic testing has shown great potential to alleviate the test oracle problem by exploiting the relations among the inputs and outputs of different executions of the system, so-called metamorphic relations (MRs). In this article, we propose a MR pattern called Performance Variation (PV) for the identification of performance-driven MRs, and we show its applicability in two CPSs from different domains: automated navigation systems and elevator control systems. For the evaluation, we assessed the effectiveness of this approach for detecting failures in an open source simulation-based autonomous navigation system, as well as in an industrial case study from the elevation domain. We derive concrete MRs based on the PV pattern for both case studies and we evaluate their effectiveness with seeded faults. Results show that the approach is effective at detecting over 88% of the seeded faults, while keeping the ratio of false positives at 4% or lower.

DOI: https://doi.org/10.1109/TR.2022.3193070

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

Title of the source: IEEE Transactions on Reliability

Publisher:  IEEE

Relevant pages:  

Year: 2022