  Stack Overflow is one of the most popular technical Q&A sites used by
software developers. Seeking help from Stack Overflow has become an essential
part of software developers' daily work for solving programming-related
questions. Although the Stack Overflow community has provided quality assurance
guidelines to help users write better questions, we observed that a significant
number of questions submitted to Stack Overflow are of low quality. In this
paper, we introduce a new web-based tool, Code2Que, which can help developers
in writing higher quality questions for a given code snippet. Code2Que consists
of two main stages: offline learning and online recommendation. In the offline
learning phase, we first collect a set of good quality <code snippet, question>
pairs as training samples. We then train our model on these training samples
via a deep sequence-to-sequence approach, enhanced with an attention mechanism,
a copy mechanism and a coverage mechanism. In the online recommendation phase,
for a given code snippet, we use the offline trained model to generate question
titles to assist less experienced developers in writing questions more
effectively. At the same time, we embed the given code snippet into a vector
and retrieve the related questions with similar problematic code snippets.
