  Stack Overflow has been heavily used by software developers to seek
programming-related information. More and more developers use Community
Question and Answer forums, such as Stack Overflow, to search for code examples
of how to accomplish a certain coding task. This is often considered to be more
efficient than working from source documentation, tutorials or full worked
examples. However, due to the complexity of these online Question and Answer
forums and the very large volume of information they contain, developers can be
overwhelmed by the sheer volume of available information. This makes it hard to
find and/or even be aware of the most relevant code examples to meet their
needs. To alleviate this issue, in this work we present a query-driven code
recommendation tool, named Que2Code, that identifies the best code snippets for
a user query from Stack Overflow posts. Our approach has two main stages: (i)
semantically-equivalent question retrieval and (ii) best code snippet
recommendation. To evaluate the performance of our proposed model, we conduct a
large scale experiment to evaluate the effectiveness of the
semantically-equivalent question retrieval task and best code snippet
recommendation task separately on Python and Java datasets in Stack Overflow.
We also perform a human study to measure how real-world developers perceive the
results generated by our model. Both the automatic and human evaluation results
demonstrate the promising performance of our model, and we have released our
code and data to assist other researchers.
