Accelerating Code Search with Deep Hashing and Code Classification

TL;DR

This paper introduces CoSHC, a deep hashing and code classification method that significantly accelerates code search processes while maintaining high accuracy, reducing retrieval time by over 90%.

Contribution

The paper presents a novel approach combining deep hashing and code classification to enhance code search efficiency without substantial accuracy loss.

Findings

Over 90% reduction in retrieval time

Maintains at least 99% of original accuracy

Effective across multiple code search models

Abstract

Code search is to search reusable code snippets from source code corpus based on natural languages queries. Deep learning-based methods of 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 an efficient code search without sacrificing too much accuracy. To evaluate the effectiveness of CoSHC, we apply our method to 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.