# Neural Code Search Revisited: Enhancing Code Snippet Retrieval through Natural Language Intent

arXiv: https://arxiv.org/abs/2008.12193 · HTML: https://mlsanity.com/paper/2008.12193/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Aug 2020.

## Built on by (most cited first, 16 of 16)

- [CoSQA: 20,000+ Web Queries for Code Search and Question Answering](https://mlsanity.com/paper/2105.13239/) (2021)
- [Unifying the Perspectives of NLP and Software Engineering: A Survey on Language Models for Code](https://mlsanity.com/paper/2311.07989/) (2023)
- [CodeRetriever: Unimodal and Bimodal Contrastive Learning for Code Search](https://mlsanity.com/paper/2201.10866/) (2022)
- [ProCQA: A Large-scale Community-based Programming Question Answering Dataset for Code Search](https://mlsanity.com/paper/2403.16702/) (2024)
- [CodeQueries: A Dataset of Semantic Queries over Code](https://mlsanity.com/paper/2209.08372/) (2022)
- [CoSQA+: Pioneering the Multi-Choice Code Search Benchmark with Test-Driven Agents](https://mlsanity.com/paper/2406.11589/) (2024)
- [Accelerating Code Search with Deep Hashing and Code Classification](https://mlsanity.com/paper/2203.15287/) (2022)
- [Noisy Pair Corrector for Dense Retrieval](https://mlsanity.com/paper/2311.03798/) (2023)
- [SPENCER: Self-Adaptive Model Distillation for Efficient Code Retrieval](https://mlsanity.com/paper/2508.00546/) (2025)
- [Natural Language-Guided Programming](https://mlsanity.com/paper/2108.05198/) (2021)
- [NS3: Neuro-Symbolic Semantic Code Search](https://mlsanity.com/paper/2205.10674/) (2022)
- [AstBERT: Enabling Language Model for Financial Code Understanding with Abstract Syntax Trees](https://mlsanity.com/paper/2201.07984/) (2022)
- [An Exploratory Literature Study on Sharing and Energy Use of Language Models for Source Code](https://mlsanity.com/paper/2307.02443/) (2023)
- [Natural Language Summarization Enables Multi-Repository Bug Localization by LLMs in Microservice Architectures](https://mlsanity.com/paper/2512.05908/) (2025)
- [Programming Language Agnostic Mining of Code and Language Pairs with Sequence Labeling Based Question Answering](https://mlsanity.com/paper/2203.10744/) (2022)
- [MQDD: Pre-training of Multimodal Question Duplicity Detection for Software Engineering Domain](https://mlsanity.com/paper/2203.14093/) (2022)

## Abstract (arXiv)

In this work, we propose and study annotated code search: the retrieval of code snippets paired with brief descriptions of their intent using natural language queries. On three benchmark datasets, we investigate how code retrieval systems can be improved by leveraging descriptions to better capture the intents of code snippets. Building on recent progress in transfer learning and natural language processing, we create a domain-specific retrieval model for code annotated with a natural language description. We find that our model yields significantly more relevant search results (with absolute gains up to 20.6% in mean reciprocal rank) compared to state-of-the-art code retrieval methods that do not use descriptions but attempt to compute the intent of snippets solely from unannotated code.