Artificial Intelligence (AI) systems, which benefit from the availability of large-scale datasets
and increasing computational power, have become effective solutions to various critical …

In this study, we consider projects for develo** service systems using machine learning
(ML) techniques. These projects involve collaboration between various stakeholders …

The development and training of deep learning models have become increasingly costly
and complex. Consequently, software engineers are adopting pre-trained models (PTMs) for …

The success of machine learning (ML) models depends on careful experimentation and
optimization of their hyperparameters. Tuning can affect the reliability and accuracy of a …

Reinforcement Learning (RL) is being increasingly used to learn and adapt application
behavior in many domains, including large-scale and safety critical systems, as for example …

Python is a popular programming language characterized by its simple syntax and easy
learning curve. Like many languages, Python has a set of best practices that should be …

The popularity of computational notebooks is rapidly increasing because of their interactive
code-output visualization and on-demand non-sequential code block execution. These …

Machine Learning (ML) has revolutionized the field of computer software development,
enabling data-based predictions and decision-making across several domains. Following …

Context: Nowadays, most deep learning frameworks (DLFs) use multilingual programming
of Python and C/C++, facilitating the flexibility and performance of the DLF. However …

Context. Modern software systems increasingly commonly contain one or multiple machine
learning (ML) components. Current development practices are generally on a trial-and-error …