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 …

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

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 …

Programmers often write code that has similarity to existing code written somewhere. A tool
that could help programmers to search such similar code would be immensely useful. Such …

The n-gram language model, which has its roots in statistical natural language processing,
has been shown to successfully capture the repetitive and predictable regularities …

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 …

n-gram statistical language model has been successfully applied to capture programming
patterns to support code completion and suggestion. However, the approaches using n …

Learning and remembering how to use APIs is difficult. While code-completion tools can
recommend API methods, browsing a long list of API method names and their …

Recent research has successfully applied the statistical n-gram language model to show
that source code exhibits a good level of repetition. The n-gram model is shown to have …

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 …