In 2006, Geoffrey Hinton proposed the concept of training “Deep Neural Networks (DNNs)”
and an improved model training method to break the bottleneck of neural network …

An increasingly popular set of techniques adopted by software engineering (SE)
researchers to automate development tasks are those rooted in the concept of Deep …

Benchmark datasets have a significant impact on accelerating research in programming
language tasks. In this paper, we introduce CodeXGLUE, a benchmark dataset to foster …

Over the last several decades, software has been woven into the fabric of every aspect of
our society. As software development surges and code infrastructure of enterprise …

While a large number of pre-trained models of source code have been successfully
developed and applied to a variety of software engineering (SE) tasks in recent years, our …

Deep learning has demonstrated its strengths in numerous binary analysis tasks, including
function boundary detection, binary code search, function prototype inference, value set …

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 …

Code embedding, as an emerging paradigm for source code analysis, has attracted much
attention over the past few years. It aims to represent code semantics through distributed …

Recent work learns contextual representations of source code by reconstructing tokens from
their context. For downstream semantic understanding tasks like summarizing code in …