RANGER -- Repository-Level Agent for Graph-Enhanced Retrieval

• URL: http://arxiv.org/abs/2509.25257v1
• Date: Sat, 27 Sep 2025 18:57:02 GMT
• Title: RANGER -- Repository-Level Agent for Graph-Enhanced Retrieval
• Authors: Pratik Shah, Rajat Ghosh, Aryan Singhal, Debojyoti Dutta,
• Abstract summary: General-purpose automated software engineering (ASE) includes tasks such as code completion, retrieval, repair, QA, and summarization.<n>These tasks require a code retrieval system that can handle specific queries about code entities, or code entity queries.<n>We present RANGER, a repository-level code retrieval agent designed to address both query types.
• Score: 0.38298911754746934
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: General-purpose automated software engineering (ASE) includes tasks such as code completion, retrieval, repair, QA, and summarization. These tasks require a code retrieval system that can handle specific queries about code entities, or code entity queries (for example, locating a specific class or retrieving the dependencies of a function), as well as general queries without explicit code entities, or natural language queries (for example, describing a task and retrieving the corresponding code). We present RANGER, a repository-level code retrieval agent designed to address both query types, filling a gap in recent works that have focused primarily on code-entity queries. We first present a tool that constructs a comprehensive knowledge graph of the entire repository, capturing hierarchical and cross-file dependencies down to the variable level, and augments graph nodes with textual descriptions and embeddings to bridge the gap between code and natural language. RANGER then operates on this graph through a dual-stage retrieval pipeline. Entity-based queries are answered through fast Cypher lookups, while natural language queries are handled by MCTS-guided graph exploration. We evaluate RANGER across four diverse benchmarks that represent core ASE tasks including code search, question answering, cross-file dependency retrieval, and repository-level code completion. On CodeSearchNet and RepoQA it outperforms retrieval baselines that use embeddings from strong models such as Qwen3-8B. On RepoBench, it achieves superior cross-file dependency retrieval over baselines, and on CrossCodeEval, pairing RANGER with BM25 delivers the highest exact match rate in code completion compared to other RAG methods.

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