Code Style Transfer

Code style transfer aims to automatically modify code's stylistic elements—like loop structures or naming conventions—while preserving its functionality. Current research focuses on adapting large language models and other neural network architectures for this task, often employing techniques like fine-tuning and adversarial training to improve robustness and accuracy. Challenges remain in accurately capturing and transferring complex stylistic features, particularly in low-resource scenarios, highlighting the need for better benchmarks and evaluation metrics. This research area has implications for improving code readability, maintainability, and security, as well as advancing the broader field of program analysis and understanding.