Effective model management is crucial for ensuring performance and reliability in Machine
Learning (ML) systems, given the dynamic nature of data and operational environments …

Abstract Self-Admitted Technical Debt (SATD), a concept highlighting sub-optimal choices in
software development documented in code comments or other project resources, poses …

Code intelligence tools such as GitHub Copilot have begun to bridge the gap between
natural language and programming language. A frequent software development task is the …

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

The development of Machine Learning (ML)-and, more recently, of Deep Learning (DL)-
intensive systems requires suitable choices, eg, in terms of technology, algorithms, and …

Machine learning (ML) components are increasingly incorporated into software products for
end-users, but developers face challenges in transitioning from ML prototypes to products …

Self-admitted technical debt (SATD) is a kind of technical debt that is already acknowledged
by the developers and needs additional work or resources to address in the future. In recent …

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

The emergence of open-source ML libraries such as TensorFlow and Google Auto ML has
enabled developers to harness state-of-the-art ML algorithms with minimal overhead …

Data Science (DS) has become a cornerstone for modern software, enabling data-driven
decisions to improve companies services. Following modern software development …