Abstract Context: A Machine Learning based System (MLS) is a software system including
one or more components that learn how to perform a task from a given data set. The …

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

The introduction of machine learning (ML) components in software projects has created the
need for software engineers to collaborate with data scientists and other specialists. While …

Context: Advancements in machine learning (ML) lead to a shift from the traditional view of
software development, where algorithms are hard-coded by humans, to ML systems …

The rapid growth of applying Machine Learning (ML) in different domains, especially in
safety-critical areas, increases the need for reliable ML components, ie, a software …

There are increasing uses of deep learning (DL) compilers to generate optimized code,
boosting the runtime performance of DL models on specific hardware. Like their traditional …

Safe deployment of self-driving cars (SDC) necessitates thorough simulated and in-field
testing. Most testing techniques consider virtualized SDCs within a simulation environment …

Deep Learning (DL) solutions are increasingly adopted, but how to test them remains a
major open research problem. Existing and new testing techniques have been proposed for …

Serverless computing is an emerging cloud computing paradigm, being adopted to develop
a wide range of software applications. It allows developers to focus on the application logic …

An increasingly popular set of techniques adopted by software engineering (SE)
researchers to automate development tasks are those rooted in the concept of Deep …