  Developers use search for various tasks such as finding code, documentation,
debugging information, etc. In particular, web search is heavily used by
developers for finding code examples and snippets during the coding process.
Recently, natural language based code search has been an active area of
research. However, the lack of real-world large-scale datasets is a significant
bottleneck. In this work, we propose a weak supervision based approach for
detecting code search intent in search queries for C# and Java programming
languages. We evaluate the approach against several baselines on a real-world
dataset comprised of over 1 million queries mined from Bing web search engine
and show that the CNN based model can achieve an accuracy of 77% and 76% for C#
and Java respectively. Furthermore, we are also releasing Search4Code, the
first large-scale real-world dataset of code search queries mined from Bing web
search engine. We hope that the dataset will aid future research on code
search.
