Repository Level Code Completion

Repository-level code completion aims to improve code generation by leveraging the entire codebase context, rather than just the immediately surrounding lines, to predict code snippets. Current research focuses on enhancing large language models (LLMs) for this task through improved context retrieval methods, often employing retrieval-augmented generation (RAG) architectures and techniques like hierarchical context pruning to manage the vast amount of information. This area is significant because it promises to substantially boost developer productivity by providing more accurate and contextually relevant code suggestions, particularly in large and complex software projects.