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 …

Abstract Machine learning (ML) is increasingly seen as a viable approach for building
compiler optimization heuristics, but many ML methods cannot replicate even the simplest of …

The advancements in machine learning techniques have encouraged researchers to apply
these techniques to a myriad of software engineering tasks that use source code analysis …

The study of graph neural networks has revealed that they can unleash new applications in
a variety of disciplines using such a basic process that we cannot imagine in the context of …

To date, being benefited from the ability of automated feature extraction and the
performance of software vulnerability identification, deep learning techniques have attracted …

With the increasing popularity of open-source software, embedded vulnerabilities have been
widely propagating to downstream software. Due to different maintenance policies, software …

Detecting parallelizable code regions is a challenging task, even for experienced
developers. Numerous recent studies have explored the use of machine learning for code …

The increasing complexity of computing systems places a tremendous burden on optimizing
compilers, requiring ever more accurate and aggressive optimizations. Machine learning …

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 …