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

Understand

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.