Code Augmentation

Code augmentation techniques aim to improve the performance of machine learning models used for various code-related tasks, such as code generation and understanding, by enhancing the quality and quantity of training data. Current research focuses on developing novel augmentation strategies, including code-to-code transformations and the incorporation of contextual information like comments and type annotations, often leveraging large language models (LLMs) and diffusion models. These advancements are significant because they address the limitations of existing datasets and improve the accuracy, robustness, and generalizability of code-understanding and code-generation models, ultimately leading to more efficient and reliable software development tools.