This review provides a comprehensive overview of the state-of-the-art methods of graph-
based networks from a deep learning perspective. Graph networks provide a generalized …

Graph neural networks provide a powerful toolkit for embedding real-world graphs into low-
dimensional spaces according to specific tasks. Up to now, there have been several surveys …

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 …

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 …

Large language models have demonstrated the ability to generate both natural language
and programming language text. Although contemporary code generation models are …

The problem of fixing errors in programs has attracted substantial interest over the years.
The key challenge for building an effective code fixing tool is to capture a wide range 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 …

Generative models have become adept at producing artifacts such as images, videos, and
prose at human-like levels of proficiency. New generative techniques, such as unsupervised …

Abstract Machine learning-based program analyses have recently shown the promise of
integrating formal and probabilistic reasoning towards aiding software development …

Learning code representations has found many uses in software engineering, such as code
classification, code search, comment generation, and bug prediction, etc. Although …