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 …

The usage of deep learning (DL) approaches for software engineering has attracted much
attention, particularly in source code modelling and analysis. However, in order to use DL …

Transformers were originally proposed as a sequence-to-sequence model for text but have
become vital for a wide range of modalities, including images, audio, video, and undirected …

Labeled data for imitation learning of theorem proving in large libraries of formalized
mathematics is scarce as such libraries require years of concentrated effort by human …

Semantic understanding of programs is a fundamental problem for programming language
processing (PLP). Recent works that learn representations of code based on pre-training …

Recent years have witnessed increasing interest in code representation learning, which
aims to represent the semantics of source code into distributed vectors. Currently, various …

We propose IR2Vec, a Concise and Scalable encoding infrastructure to represent programs
as a distributed embedding in continuous space. This distributed embedding is obtained by …

Writing optimising compilers is difficult. The range of programs that may be presented to the
compiler is huge and the systems on which they run are complex, heterogeneous, non …

Graph neural networks (GNNs) have proven to be an effective tool for vulnerability discovery
that outperforms learning-based methods working directly on source code. Unfortunately …

Deep learning methods have found successful applications in fields like image classification
and natural language processing. They have recently been applied to source code analysis …