Application of Quantum Extreme Learning Machines for QoS Prediction of Elevators’ Software in an Industrial Context-Reference-Cited by-同舟云学术

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Application of Quantum Extreme Learning Machines for QoS Prediction of Elevators’ Software in an Industrial Context

Published:2024-07-10 Issue: Volume:106 Page:399-410
ISSN:
Container-title:Companion Proceedings of the 32nd ACM International Conference on the Foundations of Software Engineering
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Author:

Wang Xinyi¹^ORCID,Ali Shaukat²^ORCID,Arrieta Aitor³^ORCID,Arcaini Paolo⁴^ORCID,Arratibel Maite⁵^ORCID

Affiliation:

1. Simula Research Laboratory, Oslo, Norway / University of Oslo, Oslo, Norway

2. Simula Research Laboratory, Oslo, Norway / Oslo Metropolitan University, Oslo, Norway

3. Mondragon University, Mondragon, Spain

4. National Institute of Informatics, Tokyo, Japan

5. Orona, San Sebastian, Spain

Funder

Research Council of Norway

Simula's internal strategic project

JST-Mirai Program

the Department of Education, Universities and Research of the Basque Country

Oslo Metropolitan University's Quantum Hub

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3663529.3663859

Reference58 articles.

1. 2024. Application of Quantum Extreme Learning Machines for QoS Prediction of Elevators’ Software in an Industrial Context. Zenodo. https://doi.org/10.5281/zenodo.11183939 10.5281/zenodo.11183939

2. K. K. Aggarwal Yogesh Singh A. Kaur and O. P. Sangwan. 2004. A neural net based approach to Test Oracle. SIGSOFT Softw. Eng. Notes 29 3 (2004) may 1–6. issn:0163-5948 https://doi.org/10.1145/986710.986725 10.1145/986710.986725

3. When software engineering meets quantum computing

4. A practical guide for using statistical tests to assess randomized algorithms in software engineering

5. Using Machine Learning to Build Test Oracles: an Industrial Case Study on Elevators Dispatching Algorithms

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