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

Cited by Lee Sonogan

PDF] Is a Single Model Enough? MuCoS: A Multi-Model Ensemble Learning for Semantic  Code Search | Semantic Scholar

Abstract by Lun Du, Xiaozhou Shi, Yanlin Wang, Ensheng Shi, Shi Han, Dongmei Zhang

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.

Publication: Cornell University (Peer-Reviewed Journal)

Pub Date: 10 Jul, 2021 Doi: https://arxiv.org/abs/2107.04773

Keywords: Computer Science, Software Engineering, Semantic Code

https://arxiv.org/abs/2107.04773 (Plenty more sections ad references in this research article)

https://www.patreon.com/GROOVYGORDS

https://entertainmentcultureonline.com/

https://ungroovygords.com/

Leave a Reply

This site uses Akismet to reduce spam. Learn how your comment data is processed.