Are elevator software robust against uncertainties? results and experiences from an industrial case study

Han, Liping; Yue, Tao; Ali, Shaukat; Arrieta, Aitor; Arratibel, Maite

doi:10.1145/3540250.3558955

Cited by 7 publications

(2 citation statements)

References 51 publications

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“…Elevator systems transport passengers in the presence of various uncertainties, e.g., software, hardware, and environment uncertainties. These uncertainties affect elevator systems in different ways, such as decreasing dispatching efficiency, worsening systems' robustness, and even threatening systems' security [21]. Among all the uncertainties, passengers' uncertainties are the most common and exist throughout the operating cycle of elevator systems.…”

Section: Discussionmentioning

confidence: 99%

Uncertainty-Aware Robustness Assessment of Industrial Elevator Systems

Han

Ali

Yue

et al. 2023

ACM Trans. Softw. Eng. Methodol.

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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.

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Section: Discussionmentioning

confidence: 99%

Uncertainty-Aware Robustness Assessment of Industrial Elevator Systems

Han

Ali

Yue

et al. 2023

ACM Trans. Softw. Eng. Methodol.

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“…Cyber-physical Systems (CPSs) play a vital role in Industry 4.0. Various CPSs have been deployed in critical infrastructures, e.g., water treatment plant [34,35] and elevator systems [16,17,36], whose safe operation concerns our daily lives. One such safetycritical CPS is railway systems (RWS).…”

Section: Introductionmentioning

confidence: 99%

KDDT: Knowledge Distillation-Empowered Digital Twin for Anomaly Detection

Xu,

Ali,

Yue

et al. 2023

Proceedings of the 31st ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Enginee

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Cyber-physical systems (CPSs), like train control and management systems (TCMS), are becoming ubiquitous in critical infrastructures. As safety-critical systems, ensuring their dependability during operation is crucial. Digital twins (DTs) have been increasingly studied for this purpose owing to their capability of runtime monitoring and warning, prediction and detection of anomalies, etc. However, constructing a DT for anomaly detection in TCMS necessitates sufficient training data and extracting both chronological and context features with high quality. Hence, in this paper, we propose a novel method named KDDT for TCMS anomaly detection. KDDT harnesses a language model (LM) and a long short-term memory (LSTM) network to extract contexts and chronological features, respectively. To enrich data volume, KDDT benefits from out-ofdomain data with knowledge distillation (KD). We evaluated KDDT with two datasets from our industry partner Alstom and obtained the F1 scores of 0.931 and 0.915, respectively, demonstrating the effectiveness of KDDT. We also explored individual contributions of the DT model, LM, and KD to the overall performance of KDDT, via a comprehensive empirical study, and observed average F1 score improvements of 12.4%, 3%, and 6.05%, respectively. CCS CONCEPTS• Software and its engineering → Maintaining software; • Security and privacy → Intrusion detection systems; • Computer systems organization → Embedded systems; Sensors and actuators; Embedded and cyber-physical systems; • Computing methodologies → Neural networks.

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