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 …

Recently, machine learning (ML) methods have been used to create powerful language
models for a broad range of natural language processing tasks. An important subset of this …

Large Language Models (LLMs) applied to code-related applications have emerged as a
prominent field, attracting significant interest from both academia and industry. However, as …

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 …

Many automated test generation techniques have been developed to aid developers with
writing tests. To facilitate full automation, most existing techniques aim to either increase …

We introduce Codex, a GPT language model fine-tuned on publicly available code from
GitHub, and study its Python code-writing capabilities. A distinct production version of Codex …

Code summarization and generation empower conversion between programming language
(PL) and natural language (NL), while code translation avails the migration of legacy code …

Deep learning has become the dominant approach in addressing various tasks in Natural
Language Processing (NLP). Although text inputs are typically represented as a sequence …

Pre-trained models for programming language have achieved dramatic empirical
improvements on a variety of code-related tasks such as code search, code completion …

Automatic program repair (APR) is crucial to improve software reliability. Recently, neural
machine translation (NMT) techniques have been used to automatically fix software bugs …