  Recent years, deep learning is increasingly prevalent in the field of
Software Engineering (SE). However, many open issues still remain to be
investigated. How do researchers integrate deep learning into SE problems?
Which SE phases are facilitated by deep learning? Do practitioners benefit from
deep learning? The answers help practitioners and researchers develop practical
deep learning models for SE tasks. To answer these questions, we conduct a
bibliography analysis on 98 research papers in SE that use deep learning
techniques. We find that 41 SE tasks in all SE phases have been facilitated by
deep learning integrated solutions. In which, 84.7% papers only use standard
deep learning models and their variants to solve SE problems. The
practicability becomes a concern in utilizing deep learning techniques. How to
improve the effectiveness, efficiency, understandability, and testability of
deep learning based solutions may attract more SE researchers in the future.
