Research at the intersection of machine learning, programming languages, and software
engineering has recently taken important steps in proposing learnable probabilistic models …

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 …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

Automatic source code summarization is the task of generating natural language
descriptions for source code. Automatic code summarization is a rapidly expanding research …

The ability to generate natural language sequences from source code snippets has a variety
of applications such as code summarization, documentation, and retrieval. Sequence-to …

During software maintenance, code comments help developers comprehend programs and
reduce additional time spent on reading and navigating source code. Unfortunately, these …

In this work we systematically review the recent advancements in software engineering with
language models, covering 70+ models, 40+ evaluation tasks, 180+ datasets, and 900 …

Source code summarization--creating natural language descriptions of source code
behavior--is a rapidly-growing research topic with applications to automatic documentation …

Neural Code Intelligence--leveraging deep learning to understand, generate, and optimize
code--holds immense potential for transformative impacts on the whole society. Bridging the …

Developers deal with code-change-related tasks daily, eg, reviewing code. Pre-trained code
and code-change-oriented models have been adapted to help developers with such tasks …