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Deep Learning (DL) has emerged in the last decade from artificial intelligence, dating from the Dartmouth conference in 1956, combined with the recent emergence of Graphical Processing Units (GPUs). These GPUs, providing a boost in computational power, initiated the popularity of artificial intelligence in everyday life applications, such as vocal personal assistants, entertainment. Deep learning techniques require large datasets either for their training phase, in the case of supervised learning neural networks, or for their learning phase in the case of unsupervised learning networks. It is common for real applications to have datasets as large as tens of thousands of data. In one of our previous study (Jahic et al., 2019; Jahic et al., 2020), by analysing the datasets and learning outcomes of the training of neural networks, we discovered that many issues were related to the poor specification of the datasets’ structure. Datasets are critical input artefacts necessary to

An MDE Method for Improving Deep Learning Dataset Requirements Engineering using Alloy and UML