  Stack Overflow has been heavily used by software developers as a popular way
to seek programming-related information from peers via the internet. The Stack
Overflow community recommends users to provide the related code snippet when
they are creating a question to help others better understand it and offer
their help. Previous studies have shown that} a significant number of these
questions are of low-quality and not attractive to other potential experts in
Stack Overflow. These poorly asked questions are less likely to receive useful
answers and hinder the overall knowledge generation and sharing process.
Considering one of the reasons for introducing low-quality questions in SO is
that many developers may not be able to clarify and summarize the key problems
behind their presented code snippets due to their lack of knowledge and
terminology related to the problem, and/or their poor writing skills, in this
study we propose an approach to assist developers in writing high-quality
questions by automatically generating question titles for a code snippet using
a deep sequence-to-sequence learning approach. Our approach is fully
data-driven and uses an attention mechanism to perform better content
selection, a copy mechanism to handle the rare-words problem and a coverage
mechanism to eliminate word repetition problem. We evaluate our approach on
Stack Overflow datasets over a variety of programming languages (e.g., Python,
Java, Javascript, C# and SQL) and our experimental results show that our
approach significantly outperforms several state-of-the-art baselines in both
automatic and human evaluation. We have released our code and datasets to
facilitate other researchers to verify their ideas and inspire the follow-up
work.
