Software engineering challenges for machine learning applications: A literature review

Machine learning techniques, especially deep learning, have achieved remarkable breakthroughs over the past decade. At present, machine learning applications are deployed in many fields. However, the outcomes of software engineering researches are not always easily utilized in the development and deployment of machine learning applications. The main reason for this difficulty is the many differences between machine learning applications and traditional information systems. Machine learning techniques are evolving rapidly, but face inherent technical and non-technical challenges that complicate their lifecycle activities. This review paper attempts to clarify the software engineering challenges for machine learning applications that either exist or potentially exist by conducting a systematic literature collection and by mapping the identified challenge topics to knowledge areas defined by the Software Engineering Body of Knowledge (Swebok).