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

This paper provides a comprehensive review of the literature concerning the utilization of
Natural Language Processing (NLP) techniques, with a particular focus on transformer …

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 …

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 …

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 …

Software engineering research has always being concerned with the improvement of code
completion approaches, which suggest the next tokens a developer will likely type while …

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

Learning tasks on source code (ie, formal languages) have been considered recently, but
most work has tried to transfer natural language methods and does not capitalize on the …

Statistical language modeling techniques have successfully been applied to large source
code corpora, yielding a variety of new software development tools, such as tools for code …

Code clone detection is an important problem for software maintenance and evolution. Many
approaches consider either structure or identifiers, but none of the existing detection …