  As a popular Q&A site for programming, Stack Overflow is a treasure for
developers. However, the amount of questions and answers on Stack Overflow make
it difficult for developers to efficiently locate the information they are
looking for. There are two gaps leading to poor search results: the gap between
the user's intention and the textual query, and the semantic gap between the
query and the post content. Therefore, developers have to constantly
reformulate their queries by correcting misspelled words, adding limitations to
certain programming languages or platforms, etc. As query reformulation is
tedious for developers, especially for novices, we propose an automated
software-specific query reformulation approach based on deep learning. With
query logs provided by Stack Overflow, we construct a large-scale query
reformulation corpus, including the original queries and corresponding
reformulated ones. Our approach trains a Transformer model that can
automatically generate candidate reformulated queries when given the user's
original query. The evaluation results show that our approach outperforms five
state-of-the-art baselines, and achieves a 5.6% to 33.5% boost in terms of
$\mathit{ExactMatch}$ and a 4.8% to 14.4% boost in terms of $\mathit{GLEU}$.
