Requirements Analysis and Decomposition for Distributed Systems based on Deep Learning (Requirements Engineering 2023 - Tutorials)

Who

Eric Knauss, Hans-Martin Heyn

Track

Requirements Engineering 2023 Tutorials

Time Zone

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When

Mon 4 Sep 2023 14:00 - 18:00 at b305 - Tutorial

Abstract

Objectives

The objectives of this tutorial are to convey to participants:

How to integrate ML concerns into architectural decomposition of high-level requirements.
How to support Middle-out development of ML-based systems.
How to support iterative, continuous, agile development of ML-based systems.
How to facilitate a systematic analysis and x-by-design of safety, security, and other system level concerns.

Link to Tutorial Material

https://github.com/martinheyn/RE23_re_breakdown_for_dl_tutorial

Schedule

Time	Topic	References
14:00 - 14:45	Theory and background (45min): RE Challenges of ML-based Systems, our architectural framework	[1], [3]
14:45 - 15:30	Tool demo and use cases (45min)	based on [2], [3], [4] and https://gitlab.com/treqs-on-git/treqs-ng
15:30 - 16:00	Break
16:00 - 16:45	Hands-on work with participants (45min)
16:45 - 17:15	Q/A and feedback collection (30min)
17:15 - 17:30	Closing

References

H.-M. Heyn, E. Knauss, and P. Pelliccione, “A compositional approach to creating architecture frameworks with an application to distributed ai systems,” Systems and Software (JSS), vol. 198, 2023. Available: https://doi.org/10.1016/j.jss.2022.111604
E. Knauss, G. Liebel, J. Horkoff, R. Wohlrab, R. Kasauli, F. Lange, and P. Gildert, “T-reqs: Tool support for managing requirements in large-scale agile system development,” in Proceedings of 26th IEEE International Requirements Engineering Conference (RE’18), Banff, Canada, 2018, pp. 502–503, tool Demo. Available: http://arxiv.org/abs/1805.02769
H.-M. Heyn, E. Knauss, A. P. Muhammad, O. Eriksson, J. Linder, P. Subbiah, S. K. Pradhan, and S. Tungal, “Requirement Engineering Challenges for AI-intense Systems Development,” in Proceedings of 1st Workshop on AI Engineering – Software Engineering for AI (WAIN), 2021. https://arxiv.org/pdf/2103.10270.pdf
M. Kaiser, R. Griessl, N. Kucza, C. Haumann, L. Tigges, K. Mika, J. Hagemeyer, F. Porrmann, U. Rückert, M. vor dem Berge, S. Krupop, M. Porrmann, M. Tassemeier, P. Trancoso, F. Qararyah, S. Zouzoula, A. Casimiro, A. Bessani, J. Cecilio, S. Andersson, O. Brunnegard, O. Eriksson, R. Weiss, F. Meierhöfer, H. Salomonsson, E. Malekzadeh, D. Ödman, A. Khurshid, P. Felber, M. Pasin, V. Schiavoni, J. Menetrey, K. Gugula, P. Zierhoffer, E. Knauss, and H.-M. Heyn: *Vedliot: Very efficient deep learning in IoT, in Proceedings of Design, Automation and Test in Europe Conference (DATE), 2022, multi-Partner Projects Track. https://arxiv.org/pdf/2207.00675.pdf

DOI

https://doi.org/10.1016/j.jss.2022.111604

Eric KnaussOrganizer

Chalmers | University of Gothenburg

Sweden

Hans-Martin HeynOrganizer

University of Gothenburg & Chalmers University of Technology