Cost-efficient pinpointing of target code contexts in complex repositories

Develop methods that cost‑efficiently pinpoint the target code contexts required to answer diverse natural language queries in complex software repositories, achieving reliable localization of relevant files, classes, and functions while minimizing token and computational overhead during repository‑level code reasoning.

Background

Repository-scale code reasoning tasks require locating small, relevant regions of large codebases to support multi-hop reasoning. Existing retrieval and graph-augmented methods improve over flat text chunking but still incur significant cost due to context size and structural complexity.

The paper highlights that, despite progress, efficiently identifying only the necessary code elements across varied query types remains unresolved, motivating approaches like FastCode that decouple exploration from full-content consumption.