  Millions of repetitive code snippets are submitted to code repositories every
day. To search from these large codebases using simple natural language queries
would allow programmers to ideate, prototype, and develop easier and faster.
Although the existing methods have shown good performance in searching codes
when the natural language description contains keywords from the code, they are
still far behind in searching codes based on the semantic meaning of the
natural language query and semantic structure of the code. In recent years,
both natural language and programming language research communities have
created techniques to embed them in vector spaces. In this work, we leverage
the efficacy of these embedding models using a simple, lightweight 2-layer
neural network in the task of semantic code search. We show that our model
learns the inherent relationship between the embedding spaces and further
probes into the scope of improvement by empirically analyzing the embedding
methods. In this analysis, we show that the quality of the code embedding model
is the bottleneck for our model's performance, and discuss future directions of
study in this area.
