  Deep-Learning(DL) applications have been widely employed to assist in various
tasks. They are constructed based on a data-driven programming paradigm that is
different from conventional software applications. Given the increasing
popularity and importance of DL applications, software engineering
practitioners have some techniques specifically for them. However, little
research is conducted to identify the challenges and lacks in practice. To fill
this gap, in this paper, we surveyed 195 practitioners to understand their
insight and experience in the software engineering practice of DL applications.
Specifically, we asked the respondents to identify lacks and challenges in the
practice of the development life cycle of DL applications. The results present
13 findings that provide us with a better understanding of software engineering
practice of DL applications. Further, we distil these findings into 7
actionable recommendations for software engineering researchers and
practitioners to improve the development of DL applications.
