# CSRS: Code Search with Relevance Matching and Semantic Matching

arXiv: https://arxiv.org/abs/2203.07736 · HTML: https://mlsanity.com/paper/2203.07736/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Mar 2022.

## Built on by (most cited first, 4 of 4)

- [Abstract Syntax Tree for Programming Language Understanding and Representation: How Far Are We?](https://mlsanity.com/paper/2312.00413/) (2023)
- [FasterPy: An LLM-based Code Execution Efficiency Optimization Framework](https://mlsanity.com/paper/2512.22827/) (2025)
- [Towards Top-Down Automated Development in Limited Scopes: A Neuro-Symbolic Framework from Expressibles to Executables](https://mlsanity.com/paper/2209.01566/) (2022)
- [Zero-Shot Cross-Domain Code Search without Fine-Tuning](https://mlsanity.com/paper/2504.07740/) (2025)

## Abstract (arXiv)

Developers often search and reuse existing code snippets in the process of software development. Code search aims to retrieve relevant code snippets from a codebase according to natural language queries entered by the developer. Up to now, researchers have already proposed information retrieval (IR) based methods and deep learning (DL) based methods. The IR-based methods focus on keyword matching, that is to rank codes by relevance between queries and code snippets, while DL-based methods focus on capturing the semantic correlations. However, the existing methods do not consider capturing two matching signals simultaneously. Therefore, in this paper, we propose CSRS, a code search model with relevance matching and semantic matching. CSRS comprises (1) an embedding module containing convolution kernels of different sizes which can extract n-gram embeddings of queries and codes, (2) a relevance matching module that measures lexical matching signals, and (3) a co-attention based semantic matching module to capture the semantic correlation. We train and evaluate CSRS on a dataset with 18.22M and 10k code snippets. The experimental results demonstrate that CSRS achieves an MRR of 0.614, which outperforms two state-of-the-art models DeepCS and CARLCS-CNN by 33.77% and 18.53% respectively. In addition, we also conducted several experiments to prove the effectiveness of each component of CSRS.