An Investigation of Challenges Encountered When Specifying Training Data and Runtime Monitors for Safety Critical ML Applications
Paper i proceeding, 2023

[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.

Data requirements

Artificial intelligence

Machine learning

Runtime monitoring


Requirements engineering


Hans-Martin Heyn

Göteborgs universitet

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Eric Knauss

Göteborgs universitet

Chalmers, Data- och informationsteknik, Interaktionsdesign och Software Engineering

Iswarya Malleswaran

Göteborgs universitet

Shruthi Dinakaran

Student vid Chalmers

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

03029743 (ISSN) 16113349 (eISSN)

Vol. 13975 LNCS 206-222
9783031297854 (ISBN)

29th International Working Conference on Requirements Engineering: Foundation for Software Quality, REFSQ 2023
Barcelona, Spain,

Very Efficient Deep Learning in IOT (VEDLIoT)

Europeiska kommissionen (EU) (EC/H2020/957197), 2020-11-01 -- 2023-10-31.


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