Representative sampling appears rare in empirical software engineering research. Not all
studies need representative samples, but a general lack of representative sampling …

Background: In recent years, Low-code development (LCD) is growing rapidly, and Gartner
and Forrester have predicted that the use of LCD is very promising. Giant companies, such …

Data scientists often develop machine learning models to solve a variety of problems in the
industry and academy but not without facing several challenges in terms of Model …

Deep learning (DL) becomes increasingly pervasive, being used in a wide range of software
applications. These software applications, named as DL based software (in short as DL …

Serverless computing is an emerging paradigm for cloud computing, gaining traction in a
wide range of applications such as video processing and machine learning. This new …

Rust is a young systems programming language designed to provide both the safety
guarantees of high-level languages and the execution performance of low-level languages …

Deep learning (DL) has been widely applied to many domains. Unique challenges in
engineering DL systems are posed by the programming paradigm shift from traditional …

Deep learning (DL) applications, built upon a heterogeneous and complex DL stack (eg,
Nvidia GPU, Linux, CUDA driver, Python runtime, and TensorFlow), are subject to software …

AI applications often use ML/DL (Machine Learning/Deep Learning) models to implement
specific AI tasks. As application developers usually are not AI experts, they often choose to …

Abstract The Workflows as Code paradigm is becoming increasingly essential to streamline
the design and management of complex processes within data-intensive software systems …