  Software developers routinely search for code using general-purpose search
engines. However, these search engines cannot find code semantically unless it
has an accompanying description. We propose a technique for semantic code
search: A Convolutional Neural Network approach to code retrieval (CoNCRA). Our
technique aims to find the code snippet that most closely matches the
developer's intent, expressed in natural language. We evaluated our approach's
efficacy on a dataset composed of questions and code snippets collected from
Stack Overflow. Our preliminary results showed that our technique, which
prioritizes local interactions (words nearby), improved the state-of-the-art
(SOTA) by 5% on average, retrieving the most relevant code snippets in the top
3 (three) positions by almost 80% of the time. Therefore, our technique is
promising and can improve the efficacy of semantic code retrieval.
