Accelerating Code Search with Deep Hashing and Code Classification
Refereed conference paper presented and published in conference proceedings


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AbstractCode search is to search reusable code snippets
from source code corpus based on natural languages queries. Deep learning-based methods
on code search have shown promising results.
However, previous methods focus on retrieval
accuracy, but lacked attention to the efficiency
of the retrieval process. We propose a novel
method CoSHC to accelerate code search with
deep hashing and code classification, aiming
to perform efficient code search without sacrificing too much accuracy. To evaluate the effectiveness of CoSHC, we apply our method
on five code search models. Extensive experimental results indicate that compared with previous code search baselines, CoSHC can save
more than 90% of retrieval time meanwhile
preserving at least 99% of retrieval accuracy.
All Author(s) ListWenchao Gu, Yanlin Wang, Lun Du, Hongyu Zhang, Shi Han, Dongmei Zhang, Michael R. Lyu
Name of ConferenceAnnual Meeting of the Association for Computational Linguistics
Start Date of Conference22/05/2022
End Date of Conference27/05/2022
Place of ConferenceDublin
Country/Region of ConferenceIreland
Proceedings TitleProceedings of the 60th Annual Meeting of the Association for Computational Linguistics Volume 1: Long Papers
Year2022
Month5
Volume Number1
Pages2534 - 2544
LanguagesEnglish-United States

Last updated on 2022-13-07 at 11:06