  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.
