Automatic code stylizing

Authors:
Steven P. Reiss
Affiliations:
Brown University, Providence, RI
Venue:
Proceedings of the twenty-second IEEE/ACM international conference on Automated software engineering
Year:
2007

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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.