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

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

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 …

This paper explores the limits of the current generation of large language models for
program synthesis in general purpose programming languages. We evaluate a collection of …

Large language models, such as OpenAI's codex and Deepmind's AlphaCode, can
generate code to solve a variety of problems expressed in natural language. This …

We present a neural model for representing snippets of code as continuous distributed
vectors (``code embeddings''). The main idea is to represent a code snippet as a single fixed …

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 …

A transcompiler, also known as source-to-source translator, is a system that converts source
code from a high-level programming language (such as C++ or Python) to another …

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 …