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 …

As Deep Neural Networks (DNNs) are rapidly being adopted within large software systems,
software developers are increasingly required to design, train, and deploy such models into …

Due to the sheer size of software logs, developers rely on automated techniques for log
analysis. One of the first and most important steps of automated log analysis is log …

The rapid escalation of applying Machine Learning (ML) in various domains has led to
paying more attention to the quality of ML components. There is then a growth of techniques …

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 …

Deep Learning (DL) techniques are increasingly being incorporated in critical software
systems today. DL software is buggy too. Recent work in SE has characterized these bugs …

Abstract Machine Learning (ML) is increasingly being adopted in different industries. Deep
Reinforcement Learning (DRL) is a subdomain of ML used to produce intelligent agents …

Abstract Artificial Intelligence for IT Operations (AIOps) leverages AI approaches to handle
the massive amount of data generated during the operations of software systems. Prior …

Deep Learning (DL) applications are being used to solve problems in critical domains (eg,
autonomous driving or medical diagnosis systems). Thus, developers need to debug their …

Context In the last decade of data-driven decision-making, Machine Learning (ML) systems
reign supreme. Because of the different characteristics between ML and traditional Software …