An empirical investigation of challenges of specifying training data and runtime monitors for critical software with machine learning and their relation to architectural decisions
Hans-Martin Heyn,
Eric Knauss,
Iswarya Malleswaran
et al.
Abstract: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. Runtime monitors for example check that the data at runtime is compatible with the data used to train the model. In a first step towards identifying challenges when specifying r… Show more
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