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, {\sc Code2Que}, which can help developers in writing higher quality questions for a given code snippet.
{\sc Code2Que} consists of two main stages: offline learning and online recommendation.
In the offline learning phase, we first collect a set of good quality $\langle$\textit{code snippet, \textit{question}}$\rangle$ pairs as training samples. We then train our model on these training samples via a deep sequence-to-sequence approach, enhanced with an \emph{attention} mechanism, a \emph{copy} mechanism and a \emph{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.
To evaluate {\sc Code2Que}, we first sampled 50 low quality $\langle$\textit{code snippet, \textit{question}}$\rangle$ pairs from the Python and Java datasets on Stack Overflow. Then we conducted a user study to evaluate the question titles generated by our approach as compared to human-written ones using three metrics: \textit{Clearness}, \textit{Fitness} and \textit{Willingness to Respond}. Our experimental results show that for a large number of low-quality questions in Stack Overflow, {\sc Code2Que} can improve the question titles in terms of \textit{Clearness}, \textit{Fitness} and \textit{Willingness} measures.