Automatic code stylizing

Abstract

Coding style is an important aspect of software development. We present a system that uses machine learning to deduce the coding style from a corpus of code and then applies this knowledge to convert arbitrary code to the learned style. We use a broad definition of coding style that includes spacing, indentation, naming, ordering, and equivalent programming constructs. The result provides a more flexible and powerful approach to code stylizing than current techniques.