REFSQ 2023
Mon 17 - Thu 20 April 2023 Barcelona, Spain
Tue 18 Apr 2023 16:00 - 16:40 at Sitges - Session R5 - RE for Artificial Intelligence Chair(s): Andreas Vogelsang

[Context and motivation] The development and operation of critical software that contains machine learning (ML) models requires diligence and established processes. Especially the training data used during the development of ML models have major influences on the later behaviour of the system. Runtime monitors are used to provide guarantees for that behaviour. [Question / problem] We see major uncertainty in how to specify training data and runtime monitoring for critical ML models and by this specifying the final functionality of the system. In this interview-based study we investigate the underlying challenges for these difficulties. [Principal ideas/results] Based on ten interviews with practitioners who develop ML models for critical applications in the automotive and telecommunication sector, we identified 17 underlying challenges in 6 challenge groups that relate to the challenge of specifying training data and runtime monitoring. [Contribution] The article provides a list of the identified underlying challenges related to the difficulties practitioners experience when specifying training data and runtime monitoring for ML models. Furthermore, interconnection between the challenges were found and based on these connections recommendation proposed to overcome the root causes for the challenges.

Tue 18 Apr

Displayed time zone: Brussels, Copenhagen, Madrid, Paris change

16:00 - 17:30
Session R5 - RE for Artificial IntelligenceResearch Papers at Sitges
Chair(s): Andreas Vogelsang University of Cologne
16:00
40m
Scientific evaluation
An investigation of challenges encountered when specifying training data and runtime monitors for safety critical ML applications
Research Papers
P: Hans-Martin Heyn University of Gothenburg & Chalmers University of Technology, A: Eric Knauss Chalmers | University of Gothenburg, A: Iswarya Malleswaran Chalmers University of Technology, A: Shruthi Dinakaran Chalmers University of Technology, D: Anastasia Mavridou KBR / NASA Ames Research Center
Pre-print
16:40
20m
Research preview
Exploring Requirements for Software that Learns: A Research Preview
Research Papers
P: Anastasia Mavridou KBR / NASA Ames Research Center, A: Marie Farrell The University of Manchester, A: Johann Schumann KBR / NASA Ames Research Center, D: Xavier Franch Universitat Politècnica de Catalunya
17:00
20m
Vision and Emerging Results
A Requirements Engineering Perspective to AI-based Systems Development: A Vision Paper
Research Papers
P: Xavier Franch Universitat Politècnica de Catalunya, A: Andreas Jedlitschka Fraunhofer, A: Silverio Martínez-Fernández UPC-BarcelonaTech, D: Hans-Martin Heyn University of Gothenburg & Chalmers University of Technology