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

In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

Since 2009, the deep learning revolution, which was triggered by the introduction of
ImageNet, has stimulated the synergy between Software Engineering (SE) and Machine …

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 …

Recent years have seen the successful application of deep learning to software engineering
(SE). In particular, the development and use of pre-trained models of source code has …

This study explores the application of deep learning technologies in software development
processes, particularly in automating code reviews, error prediction, and test generation to …

While deep learning is an exciting new technique, the benefits of this method need to be
assessed with respect to its computational cost. This is particularly important for deep …

Deep learning (DL) training algorithms utilize nondeterminism to improve models' accuracy
and training efficiency. Hence, multiple identical training runs (eg, identical training data …

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 …

Deep learning has gained tremendous traction from the developer and researcher
communities. It plays an increasingly significant role in a number of application domains …