Federated learning is an emerging machine learning paradigm where clients train models
locally and formulate a global model based on the local model updates. To identify the state …

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 …

Deployed AI systems often do not work. They can be constructed haphazardly, deployed
indiscriminately, and promoted deceptively. However, despite this reality, scholars, the …

AI-based systems are software systems with functionalities enabled by at least one AI
component (eg, for image-, speech-recognition, and autonomous driving). AI-based systems …

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 …

The development of AI applications is a multidisciplinary effort, involving multiple roles
collaborating with the AI developers, an umbrella term we use to include data scientists and …

Background: Developing and maintaining large scale machine learning (ML) based
software systems in an industrial setting is challenging. There are no well-established …

Background. The increasing reliance on applications with machine learning (ML)
components calls for mature engineering techniques that ensure these are built in a robust …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

Identifying security bug reports (SBRs) accurately from a bug repository can reduce a
software product's security risk. However, the class imbalance problem exists for SBR …