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 …

Deep Learning (DL) techniques for Natural Language Processing have been evolving
remarkably fast. Recently, the DL advances in language modeling, machine translation, and …

Pre-trained models have been shown effective in many code intelligence tasks. These
models are pre-trained on large-scale unlabeled corpus and then fine-tuned in downstream …

Millions of open source projects with numerous bug fixes are available in code repositories.
This proliferation of software development histories can be leveraged to learn how to fix …

There have been multiple recent proposals on using deep neural networks for code search
using natural language. Common across these proposals is the idea of embedding code …

Millions of open-source projects with numerous bug fixes are available in code repositories.
This proliferation of software development histories can be leveraged to learn how to fix …

Assessing the similarity between code components plays a pivotal role in a number of
Software Engineering (SE) tasks, such as clone detection, impact analysis, refactoring, etc …

Code search is a core software engineering task. Effective code search tools can help
developers substantially improve their software development efficiency and effectiveness. In …

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

To improve software reliability, software defect prediction is used to find software bugs and
prioritize testing efforts. Recently, some researchers introduced deep learning models, such …