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

In software development through integrated development environments (IDEs), code
completion is one of the most widely used features. Nevertheless, majority of integrated …

Distribution shifts—where the training distribution differs from the test distribution—can
substantially degrade the accuracy of machine learning (ML) systems deployed in the wild …

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 …

Every programmer has a characteristic style, ranging from preferences about identifier
naming to preferences about object relationships and design patterns. Coding conventions …

In this paper, we propose a novel end-to-end approach for AI-assisted code completion
called Pythia. It generates ranked lists of method and API recommendations which can be …

Writing tests is a time-consuming yet essential task during software development. We
propose to leverage recent advances in deep learning for text and code generation to assist …

Code completion aims at speeding up code writing by predicting the next code token (s) the
developer is likely to write. Works in this field focused on improving the accuracy of the …

Code completion has become an essential component of integrated development
environments. Contemporary code completion methods rely on the abstract syntax tree …

Code completion is one of the main features of modern Integrated Development
Environments (IDEs). Its objective is to speed up code writing by predicting the next code …