This document summarizes a study on requirement management tools for developing modern AI-based software. The study evaluated several open-source requirement management tools based on criteria such as extensibility, traceability, and GDPR compliance. While open-source tools provide flexibility, they often lack detailed documentation and the ability to handle regulatory requirements. Additionally, no single tool could adequately capture AI requirements from different stakeholder perspectives. The study concludes that developing trustworthy AI involves coordination among multiple groups, which current tools do not fully support.

1. One to Rule them All: A Study on Requirement
Management Tools for the Development of
Modern AI-based Software
A. Ottun, M. Asadi, M. Boerger, N. Tcholtchev, J.F.F Gongalves,
D. Borovĉanin, B. Siniarski, and H. Flores
This research is part of SPATIAL project that has received funding from the European Union’s Horizon 2020
research and innovation programme under grant agreement No.101021808.
IEEE Big Data, Sorrento, Italy, December 15, 2023

2. Our Team
Collaborators

3. Modern applications
[IEEE-PC] Z. Yin, M. Olapade, M. Liyanage, F.
Dar, A. Zuniga, N. H. Motlagh, X. Su, S. Tarkoma,
P. Hui, P. Nurmi, H. Flores: Toward City-Scale
Litter Monitoring using Autonomous Ground
Vehicles, IEEE Pervasive Computing Magazine,
[source] Ottun, Abdul-Rasheed., &.. Flores, Huber. (2023). Detection mechanisms to identify data biases and exploratory studies about
different data quality trade-offs. Deliverable 3.1, H2020 EU SPATIAL, 2023

4. System architectures
[source] Ottun, Abdul-Rasheed., &.. Flores, Huber. (2023). Detection mechanisms to identify data biases and exploratory
studies about different data quality trade-offs. Deliverable 3.1, H2020 EU SPATIAL, 2023

5. System architectures
[source] Ottun, Abdul-Rasheed., &.. Flores, Huber. (2023). Detection mechanisms to identify data biases and exploratory
studies about different data quality trade-offs. Deliverable 3.1, H2020 EU SPATIAL, 2023

6. System architectures
[source] Ottun, Abdul-Rasheed., &.. Flores, Huber. (2023). Detection mechanisms to identify data biases and exploratory
studies about different data quality trade-offs. Deliverable 3.1, H2020 EU SPATIAL, 2023

7. Modern system architectures
Cyber-threats
• Data poisoning
• Model evasion
• Situational
perturbations
Analysis
• Transparency
• Resilience
• Accountability

8. Modern applications
[IEEE-PC] Z. Yin, M. Olapade, M. Liyanage, F.
Dar, A. Zuniga, N. H. Motlagh, X. Su, S. Tarkoma,
P. Hui, P. Nurmi, H. Flores: Toward City-Scale
Litter Monitoring using Autonomous Ground
Vehicles, IEEE Pervasive Computing Magazine,
[source] Ottun, Abdul-Rasheed., &.. Flores, Huber. (2023). Detection mechanisms to identify data biases and exploratory studies about
different data quality trade-offs. Deliverable 3.1, H2020 EU SPATIAL, 2023

9. Modern applications
[source]
https://www.reddit.com/r/europe/comments/r
752a1/first_robot_traffic_jam_recorded_in_e
stonia/
[source] https://gbagenlaw.com/drone-related-injuries-
are-becoming-more-common/
[source]
http://www.iretron.com/blog/posts/where-are-
the-delivery-drones/drone-accident/
[source] Ottun, Abdul-Rasheed., &.. Flores, Huber. (2023). Detection mechanisms to identify data biases and exploratory studies about
different data quality trade-offs. Deliverable 3.1, H2020 EU SPATIAL, 2023

10. SPATIAL
SPATIAL design an development

11. Requirements elicitation

12. Requirements elicitation

13. Requirement management tools
COTS methodology
(off-the-shelf, software as
it is )
- Criteria defined using
- ISO/IEC TR 24766:2009
- Extensibility
- Local installation
- Traceability
- GDPR compliance
- Export capability

14. Requirement management tools
Tools selected
- OSRMT
- RMTOO
- OpenReq
- Doorstop
- CAIRIS

15. Results

16. Results

17.  Local and internal regulations of companies/institutions also makes it
difficult to choose a tool
 Open source tools lack detailed documentation, increasing their
learning curve
 Open source tools could be adopted to comply with regulations,
however, low level instrumentation is required, increasing the cost of
adoption
 AI trustworthiness is an on-going process that involves stakeholders,
users, developers and policy makers, RM tools are unable to handle the
definition of AI requirements from specific angles/terminologies
Conclusions

18. ACKNOWLEDGEMENTS
This research is part of SPATIAL project that has received
funding from the European Union’s Horizon 2020 research
and innovation programme under grant agreement
No.101021808.

19. MOOC courses completely free! Equivalent to 2 ECTS (Content free for anyone, certificate option
also)
MinnaLearn “Elements of AI” (In partnership with University of Tartu, TU Delft, UCD and Erasmus)
Trustworthy AI Sign up!

20. MOOC courses completely free! Equivalent to 2 ECTS (Content free for anyone, certificate option
also)
MinnaLearn “Elements of AI” (In partnership with University of Tartu, TU Delft, UCD and Erasmus)
Trustworthy AI Sign up!

21. Thank you
Huber Flores
huber.flores@ut.ee
http://spatial-h2020.eu
This project has received funding from the European Union’s Horizon 2020 research and innovation
programme under grant agreement N° 101021808.