# Accelerating Code Search with Deep Hashing and Code Classification

arXiv: https://arxiv.org/abs/2203.15287 · HTML: https://mlsanity.com/paper/2203.15287/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 32 references that are in the map.
- First posted on arXiv 29 Mar 2022.

## Builds on (most cited first, 15 of 15)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [On the Properties of Neural Machine Translation: Encoder-Decoder Approaches](https://mlsanity.com/paper/1409.1259/) (2014)
- [CodeSearchNet Challenge: Evaluating the State of Semantic Code Search](https://mlsanity.com/paper/1909.09436/) (2019)
- [GraphCodeBERT: Pre-training Code Representations with Data Flow](https://mlsanity.com/paper/2009.08366/) (2020)
- [HashNet: Deep Learning to Hash by Continuation](https://mlsanity.com/paper/1702.00758/) (2017)
- [A Survey on Deep Hashing Methods](https://mlsanity.com/paper/2003.03369/) (2020)
- [When Deep Learning Met Code Search](https://mlsanity.com/paper/1905.03813/) (2019)
- [Learning to Hash for Indexing Big Data - A Survey](https://mlsanity.com/paper/1509.05472/) (2015)
- [CoaCor: Code Annotation for Code Retrieval with Reinforcement Learning](https://mlsanity.com/paper/1904.00720/) (2019)
- [A Multi-Perspective Architecture for Semantic Code Search](https://mlsanity.com/paper/2005.06980/) (2020)
- [Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent](https://mlsanity.com/paper/2008.12193/) (2020)
- [Unsupervised Generative Adversarial Cross-modal Hashing](https://mlsanity.com/paper/1712.00358/) (2017)
- [CRaDLe: Deep Code Retrieval Based on Semantic Dependency Learning](https://mlsanity.com/paper/2012.01028/) (2020)
- [Deep Binary Reconstruction for Cross-modal Hashing](https://mlsanity.com/paper/1708.05127/) (2017)

## Built on by (most cited first, 10 of 10)

- [Agents in Software Engineering: Survey, Landscape, and Vision](https://mlsanity.com/paper/2409.09030/) (2024)
- [CoCoSoDa: Effective Contrastive Learning for Code Search](https://mlsanity.com/paper/2204.03293/) (2022)
- [Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit](https://mlsanity.com/paper/2401.00288/) (2023)
- [AlignCoder: Aligning Retrieval with Target Intent for Repository-Level Code Completion](https://mlsanity.com/paper/2601.19697/) (2026)
- [SpecAgent: A Speculative Retrieval and Forecasting Agent for Code Completion](https://mlsanity.com/paper/2510.17925/) (2025)
- [DRAINCODE: Stealthy Energy Consumption Attacks on Retrieval-Augmented Code Generation via Context Poisoning](https://mlsanity.com/paper/2601.20615/) (2026)
- [PhoenixRepair: Rethinking Repair Strategy Exploration in Software Agents](https://mlsanity.com/paper/2607.18859/) (2026)
- [DIGMN: Dynamic Intent Guided Meta Network for Differentiated User Engagement Forecasting in Online Professional Social Platforms](https://mlsanity.com/paper/2210.12402/) (2022)
- [DEMO: A Statistical Perspective for Efficient Image-Text Matching](https://mlsanity.com/paper/2405.11496/) (2024)
- [XSearch: Explainable Code Search via Concept-to-Code Alignment](https://mlsanity.com/paper/2605.16046/) (2026)

## Abstract (arXiv)

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.