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Deep learning models struggle with large-scale code retrieval, new paper finds

By PulseAugur Editorial · [2 sources] ·

A new research paper titled "Recall Before Rerank" evaluates the performance of deep learning models for large-scale code-to-code retrieval. The study highlights limitations in the precision and scalability of current models when dealing with terabyte-scale source code collections across various programming languages. Researchers propose LLM-based techniques for code normalization and query rewriting to improve the performance of less effective models, questioning the viability of resource-constrained deployments for specialized code LLMs. AI

IMPACT Highlights limitations in current LLMs for code retrieval, suggesting areas for improvement in scalability and precision for developers.

RANK_REASON The cluster contains a research paper published on arXiv.

Read on arXiv cs.IR (Information Retrieval) →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Deep learning models struggle with large-scale code retrieval, new paper finds

COVERAGE [2]

  1. arXiv cs.CL TIER_1 English(EN) · Leonardo Venuta, Francesco Tosoni, Paolo Ferragina ·

    Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval

    arXiv:2606.27401v1 Announce Type: cross Abstract: Semantic code search and clone detection are essential for software development, maintenance, and reuse. This paper evaluates the effectiveness, efficiency, and scalability of contemporary deep learning models for first-stage reca…

  2. arXiv cs.IR (Information Retrieval) TIER_1 English(EN) · Paolo Ferragina ·

    Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval

    Semantic code search and clone detection are essential for software development, maintenance, and reuse. This paper evaluates the effectiveness, efficiency, and scalability of contemporary deep learning models for first-stage recall in large-scale code-to-code search engines. Ben…

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