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Evaluates retrieval strategies for multi-file code context understanding in programming languages, suggesting improved performance metrics.
Retrieval-Augmented Generation (RAG) is increasingly employed to ground large language models in vast code repositories. However, performance degrades when resolving repository-level dependencies and multi-file code contexts. This study evaluates dense, sparse (BM25), and hybrid Reciprocal Rank Fusion (RRF) retrieval strategies across Python, Java, and C++ codebases using an LLM-as-a-Judge evaluation framework.
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Anees Rehman (2026) studied this question.
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