AIRCoder: Adaptive Integration of Multi-dimensional Retrieval for Repository-level Code Completion

AbstractRepository-level code completion relies on retrieval strategies to select relevant context from large codebases. Most existing methods employ single-dimensional retrieval based on textual similarity or dependency existence, leading to inconsistent performance across completion scenarios. Even task-adaptive and hybrid methods incur high computational costs while failing to explicitly consider structural hierarchy for retrieving functionally similar code. We introduce **AIRCoder**, a multi-dimensional retrieval framework that combines eight complementary metrics across three dimensions: textual similarity, dependency existence, and structural hierarchy. By proposing a structure-preserving chunking strategy and lightweight fusion module, AIRCoder learns context-dependent weights to adaptively integrate retrieval metrics for each query. Experiments on CrossCodeEval and RepoEval demonstrate that AIRCoder achieves an average improvement of **4.63%** in exact match over the best baseline, with **10.2×** higher efficiency and strong cross-language generalization across Python, Java, C#, and TypeScript.