Code Generation

Code generation research focuses on using large language models (LLMs) to automatically produce functional and secure code from natural language descriptions or other inputs. Current efforts concentrate on improving the accuracy and efficiency of code generation, including developing novel training objectives like horizon-length prediction and employing techniques such as multi-agent frameworks, Monte Carlo Tree Search, and prompt engineering to guide LLMs towards better solutions. This field is significant because it promises to dramatically increase developer productivity and accelerate software development, while also raising important questions about code security and reliability that require further investigation.