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Traceable business-to-safety analysis framework for safety-critical machine learning systems

Authors:

Hironori TakeuchiAuthors Info & Claims

CAIN '22: Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI

Pages 50 - 51

https://doi.org/10.1145/3522664.3528619

Published: 17 October 2022 Publication History

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Abstract

Machine learning-based system requires specific attention towards their safety characteristics while considering the higher-level requirements. This study describes our approach for analyzing machine learning safety requirements top-down from higher-level business requirements, functional requirements, and risks to be mitigated. Our approach utilizes six different modeling techniques: AI Project Canvas, Machine Learning Canvas, KAOS Goal Modeling, UML Components Diagram, STAMP/STPA, and Safety Case Analysis. As a case study, we also demonstrated our approach for lane and other vehicle detection functions of self-driving cars.

References

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Tatiana Chuprina, Daniel Mendez, and Krzysztof Wnuk. 2021. Towards Artefact-based Requirements Engineering for Data-Centric Systems. http://ceur-ws.org

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Jati H. Husen, Hnin Thandar Tun, Nobukazu Yoshioka, Hironori Washizaki, and Yoshiaki Fukazawa. 2021. Goal-Oriented Machine Learning-Based Component Development Process. Companion Proceedings - 24th International Conference on Model-Driven Engineering Languages and Systems, MODELS-C 2021, 643--644.

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Sina Mohseni, Mandar Pitale, Vasu Singh, and Zhangyang Wang. 2020. Practical Solutions for Machine Learning Safety in Autonomous Vehicles.

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Hnin Thandar Tun, Jati H. Husen, Nobukazu Yoshioka, Hironori Washizaki, and Yoshiaki Fukazawa. 2021. Goal-Centralized Metamodel Based Requirements Integration for Machine Learning Systems. 2021 28th Asia-Pacific Software Engineering Conference Workshops (APSEC Workshops), 13--16.

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Cited By

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Habiba UHaug MBogner JWagner S(2024)How mature is requirements engineering for AI-based systems? A systematic mapping study on practices, challenges, and future research directionsRequirements Engineering10.1007/s00766-024-00432-329:4(567-600)Online publication date: 23-Oct-2024
https://doi.org/10.1007/s00766-024-00432-3
Tanaka HIde MMunakata KWashizaki HYoshioka N(2023)Activity-based modeling strategy for reliable machine learning system analysis targeting GUI-based applications2023 10th International Conference on Dependable Systems and Their Applications (DSA)10.1109/DSA59317.2023.00026(135-143)Online publication date: 10-Aug-2023
https://doi.org/10.1109/DSA59317.2023.00026
Neto ACamargo JAlmeida JCugnasca P(2022)Safety Assurance of Artificial Intelligence-Based Systems: A Systematic Literature Review on the State of the Art and Guidelines for Future WorkIEEE Access10.1109/ACCESS.2022.322923310(130733-130770)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3229233

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Information

Published In

CAIN '22: Proceedings of the 1st International Conference on AI Engineering: Software Engineering for AI

May 2022

254 pages

ISBN:9781450392754

DOI:10.1145/3522664

General Chair:
Ivica Crnkovic
Chalmers University of Technology, SE

This work is licensed under a Creative Commons Attribution International 4.0 License.

In-Cooperation

IEEE TCSC: IEEE Technical Committee on Scalable Computing

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 17 October 2022

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Short-paper

Funding Sources

JST-Mirai Program
JST SPRING

Conference

CAIN '22

Sponsor:

SIGSOFT

CAIN '22: 1st Conference on AI Engineering - Software Engineering for AI

May 16 - 24, 2022

Pennsylvania, Pittsburgh

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Cited By

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Habiba UHaug MBogner JWagner S(2024)How mature is requirements engineering for AI-based systems? A systematic mapping study on practices, challenges, and future research directionsRequirements Engineering10.1007/s00766-024-00432-329:4(567-600)Online publication date: 23-Oct-2024
https://doi.org/10.1007/s00766-024-00432-3
Tanaka HIde MMunakata KWashizaki HYoshioka N(2023)Activity-based modeling strategy for reliable machine learning system analysis targeting GUI-based applications2023 10th International Conference on Dependable Systems and Their Applications (DSA)10.1109/DSA59317.2023.00026(135-143)Online publication date: 10-Aug-2023
https://doi.org/10.1109/DSA59317.2023.00026
Neto ACamargo JAlmeida JCugnasca P(2022)Safety Assurance of Artificial Intelligence-Based Systems: A Systematic Literature Review on the State of the Art and Guidelines for Future WorkIEEE Access10.1109/ACCESS.2022.322923310(130733-130770)Online publication date: 2022
https://doi.org/10.1109/ACCESS.2022.3229233

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Requirement traceability in safety critical systems

A Comparative Safety Assessment Approach for Safety Critical Systems

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