# Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic Code Search

arXiv: https://arxiv.org/abs/2107.04773 · HTML: https://mlsanity.com/paper/2107.04773/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 10 Jul 2021.

## Builds on (most cited first, 12 of 12)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [CodeBERT: A Pre-Trained Model for Programming and Natural Languages](https://mlsanity.com/paper/2002.08155/) (2020)
- [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)
- [When Deep Learning Met Code Search](https://mlsanity.com/paper/1905.03813/) (2019)
- [On the Generalizability of Neural Program Models with respect to Semantic-Preserving Program Transformations](https://mlsanity.com/paper/2008.01566/) (2020)
- [A Multi-Perspective Architecture for Semantic Code Search](https://mlsanity.com/paper/2005.06980/) (2020)
- [Deep Graph Matching and Searching for Semantic Code Retrieval](https://mlsanity.com/paper/2010.12908/) (2020)
- [Leveraging Code Generation to Improve Code Retrieval and Summarization via Dual Learning](https://mlsanity.com/paper/2002.10198/) (2020)
- [OCoR: An Overlapping-Aware Code Retriever](https://mlsanity.com/paper/2008.05201/) (2020)
- [Tag2Vec: Learning Tag Representations in Tag Networks](https://mlsanity.com/paper/1905.03041/) (2019)

## Built on by (most cited first, 1 of 1)

- [GraphSearchNet: Enhancing GNNs via Capturing Global Dependencies for Semantic Code Search](https://mlsanity.com/paper/2111.02671/) (2021)

## Abstract (arXiv)

Recently, deep learning methods have become mainstream in code search since they do better at capturing semantic correlations between code snippets and search queries and have promising performance. However, code snippets have diverse information from different dimensions, such as business logic, specific algorithm, and hardware communication, so it is hard for a single code representation module to cover all the perspectives. On the other hand, as a specific query may focus on one or several perspectives, it is difficult for a single query representation module to represent different user intents. In this paper, we propose MuCoS, a multi-model ensemble learning architecture for semantic code search. It combines several individual learners, each of which emphasizes a specific perspective of code snippets. We train the individual learners on different datasets which contain different perspectives of code information, and we use a data augmentation strategy to get these different datasets. Then we ensemble the learners to capture comprehensive features of code snippets.