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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
Our Team
Collaborators
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
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
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
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
Modern system architectures
Cyber-threats
• Data poisoning
• Model evasion
• Situational
perturbations
Analysis
• Transparency
• Resilience
• Accountability
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
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
SPATIAL
SPATIAL design an development
Requirements elicitation
Requirements elicitation
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
Requirement management tools
Tools selected
- OSRMT
- RMTOO
- OpenReq
- Doorstop
- CAIRIS
Results
Results
 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
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.
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!
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!
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.

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One-to-rule-them-all_BigData2023_Ottun.pdf

  • 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
  • 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
  • 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
  • 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.