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

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 …

Over the past decade, predictive language modeling for code has proven to be a valuable
tool for enabling new forms of automation for developers. More recently, we have seen the …

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 …

Automatic program repair (APR) is crucial to improve software reliability. Recently, neural
machine translation (NMT) techniques have been used to automatically fix software bugs …

Static bug detection has shown its effectiveness in detecting well-defined memory errors, eg,
memory leaks, buffer overflows, and null dereference. However, modern software systems …

The constantly increasing number of disclosed security vulnerabilities have become an
important concern in the software industry and in the field of cybersecurity, suggesting that …

Automated generate-and-validate (GV) program repair techniques (APR) typically rely on
hard-coded rules, thus only fixing bugs following specific fix patterns. These rules require a …

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 …

The automatic detection of software vulnerabilities is an important research problem.
However, existing solutions to this problem rely on human experts to define features and …