Code Summarization

Code summarization aims to automatically generate concise natural language descriptions of source code, improving code understanding and maintainability. Current research heavily utilizes large language models (LLMs), often within encoder-decoder architectures or enhanced with techniques like prompt engineering, retrieval-augmented mechanisms, and multi-task learning, to improve summary quality and address challenges like handling diverse programming languages and code structures. This field is significant because effective code summarization can significantly reduce the time and effort required for software development, maintenance, and comprehension, impacting both research and practical software engineering workflows.