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August 7, 2026Open Access

Evaluating Retrieval-Augmented Generation Strategies for Repository-Level Code Understanding

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Authors

ARAnees Rehman

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Overview

Randomized trial evaluates retrieval strategies for multi-file code context understanding in programming languages, suggesting improved performance metrics.

Key Points

  • This research aims to assess the effectiveness of various retrieval strategies for understanding code in large repositories.
  • Evaluated dense, sparse (BM25), and hybrid reciprocal rank fusion (RRF) retrieval strategies.
  • Applied the LLM-as-a-Judge framework for performance assessment.
  • Analyzed codebases in Python, Java, and C++ to determine strategy effectiveness.
  • Found that hybrid RRF retrieval outperforms dense and sparse strategies in multi-file contexts.
  • Performance improvements were significant, especially for complex repository dependencies.
  • Highlighted variations in effectiveness across different programming languages.

Cite This Study

Anees Rehman (2026) studied this question.

synapsesocial.com/papers/6a758c0f847ab6d26c01fd5bhttps://doi.org/10.5281/zenodo.21813118
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