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

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 …

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

Recent years, deep learning is increasingly prevalent in the field of Software Engineering
(SE). However, many open issues still remain to be investigated. How do researchers …

Context: The software development industry is rapidly adopting machine learning for
transitioning modern day software systems towards highly intelligent and self-learning …

Deep Learning (DL) is being used nowadays in many traditional Software Engineering (SE)
problems and tasks. However, since the renaissance of DL techniques is still very recent, we …

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 …

Machine learning (ML) techniques increase the effectiveness of software engineering (SE)
lifecycle activities. We systematically collected, quality-assessed, summarized, and …

Abstract Machine learning techniques, especially deep learning, have achieved remarkable
breakthroughs over the past decade. At present, machine learning applications are …

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 …