CodeStylist: A System for Performing Code Style Transfer Using Neural Networks

Code style refers to attributes of computer programs that affect their readability, maintainability, and performance. Enterprises consider code style as important and enforce style requirements during code commits. Tools that assist in coding style compliance and transformations are highly valuable. However, many key aspects of programming style transfer are difficult to automate, as it can be challenging to specify the patterns required to perform the transfer algorithmically. In this paper, we describe a system called CodeStylist which uses neural methods to perform style transfer on code.

Authors

• Chih-Kai Ting University of California Santa Cruz
• Karl Munson University of California Santa Cruz
• Serenity Wade University of California Santa Cruz
• Anish Savla University of California Santa Cruz
• Kiran Kate IBM Research
• Kavitha Srinivas IBM Research

Keywords

• Code Style
• CodeT5
• Generation
• Language Model Probing
• Pre-Trained Language Model
• Python