重排前召回：面向大规模代码到代码检索的深度学习模型基准测试

Computer Science > Software Engineering arXiv:2606.27401 (cs) [Submitted on 24 Jun 2026 (v1), last revised 18 Sep 2026 (this version, v2)] Title:Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval Authors:Leonardo Venuta, Francesco Tosoni, Paolo Ferragina View a PDF of the paper titled Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval, by Leonardo Venuta and 2 other authors View PDF HTML (experimental) 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 recall in large-scale code-to-code search engines. Benchmarking across multiple programming languages and datasets reveals critical limits in the precision and scalability of these models on Terabyte-scale source-code collections. We present LLM-based code normalisation and query-rewriting schemes that yield significant gains in precision for lower-performing models. Our results question the sustainability of resource-constrained deployment and the assumed robustness of current code-specialised LLMs across datasets. We conclude with actionable insights for building scalable, efficient code-retrieval systems. Comments: Subjects: Software Engineering (cs.SE); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG) MSC classes: 68P20, 68T50, 68T07 ACM classes: H.3.3; D.2.13; I.2.7 Cite as: arXiv:2606.27401 [cs.SE] (or arXiv:2606.27401v2 [cs.SE] for this version) https://doi.org/10.48550/arXiv.2606.27401 Focus to learn more arXiv-issued DOI via DataCite Submission history From: Francesco Tosoni [view email] [v1] Wed, 24 Jun 2026 13:41:36 UTC (258 KB) [v2] Fri, 18 Sep 2026 12:37:26 UTC (258 KB) Full-text links: Access Paper: View a PDF of the paper titled Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval, by Leonardo Venuta and 2 other authors View PDF HTML (experimental) TeX Source view license Current browse context: cs.SE new | recent | 2026-06 Change to browse by: cs cs.CL cs.IR cs.LG References & Citations NASA ADS Google Scholar Semantic Scholar export BibTeX citation Loading... BibTeX formatted citation × loading... Data provided by: Bookmark Bibliographic Tools Bibliographic and Citation Tools Bibliographic Explorer Toggle Bibliographic Explorer (What is the Explorer?) Connected Papers Toggle Connected Papers (What is Connected Papers?) Litmaps Toggle Litmaps (What is Litmaps?) scite.ai Toggle scite Smart Citations (What are Smart Citations?) Code, Data, Media Code, Data and Media Associated with this Article alphaXiv Toggle alphaXiv (What is alphaXiv?) Links to Code Toggle CatalyzeX Code Finder for Papers (What is CatalyzeX?) DagsHub Toggle DagsHub (What is DagsHub?) GotitPub Toggle Gotit.pub (What is GotitPub?) Huggingface Toggle Hugging Face (What is Huggingface?) ScienceCast Toggle ScienceCast (What is ScienceCast?) Demos Demos Replicate Toggle Replicate (What is Replicate?) Spaces Toggle Hugging Face Spaces (What is Spaces?) Spaces Toggle TXYZ.AI (What is TXYZ.AI?) Related Papers Recommenders and Search Tools Link to Influence Flower Influence Flower (What are Influence Flowers?) Core recommender toggle CORE Recommender (What is CORE?) Author Venue Institution Topic About arXivLabs arXivLabs: experimental projects with community collaborators arXivLabs is a framework that allows collaborators to develop and share new arXiv features directly on our website. Both individuals and organizations that work with arXivLabs have embraced and accepted our values of openness, community, excellence, and user data privacy. arXiv is committed to these values and only works with partners that adhere to them. Have an idea for a project that will add value for arXiv's community? Learn more about arXivLabs. Which authors of this paper are endorsers? | Disable MathJax (What is MathJax?)