Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval
Mirrored from arXiv — Machine Learning for archival readability. Support the source by reading on the original site.
Computer Science > Software Engineering
Title:Recall Before Rerank: Benchmarking Deep Learning Models for Large-Scale Code-to-Code Retrieval
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: | 15 pages, 4 figures |
| 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:2606.27401v1 [cs.SE] for this version) | |
| https://doi.org/10.48550/arXiv.2606.27401
arXiv-issued DOI via DataCite (pending registration)
|
Access Paper:
- View PDF
- HTML (experimental)
- TeX Source
Current browse context:
References & Citations
Bookmark
Bibliographic and Citation Tools
Code, Data and Media Associated with this Article
Demos
Recommenders and Search Tools
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.
More from arXiv — Machine Learning
-
Can AI Draw Science? A Benchmark for Evaluating Scientific Figure Generation by Text-to-Image and Multimodal Models
Jun 30
-
On the Necessity of a Liquid Substrate for Mesh Intelligence
Jun 30
-
Position: RL Researchers Need to Distinguish Between Solving Simulators and Using Simulators as a Proxy
Jun 30
-
Learning to Distributedly Estimate under Partially Known Dynamics: A Covariance-Agnostic Neural Kalman Consensus Filter
Jun 30
Discussion (0)
Sign in to join the discussion. Free account, 30 seconds — email code or GitHub.
Sign in →No comments yet. Sign in and be the first to say something.