Interpreting cloud computer vision pain-points: a mining study of stack overflow

Intelligent services are becoming increasingly more pervasive; application developers want to leverage the latest advances in areas
such as computer vision to provide new services and products to
users, and large technology firms enable this via RESTful APIs.
While such APIs promise an easy-to-integrate on-demand machine
intelligence, their current design, documentation and developer interface hides much of the underlying machine learning techniques
that power them. Such APIs look and feel like conventional APIs
but abstract away data-driven probabilistic behaviour—the implications of a developer treating these APIs in the same way as other,
traditional cloud services, such as cloud storage, is of concern. The
objective of this study is to determine the various pain-points developers face when implementing systems that rely on the most
mature of these intelligent services, specifically those that provide
computer vision. We use Stack Overflow to mine indications of the
frustrations that developers appear to face when using computer
vision services, classifying their questions against two recent classification taxonomies (documentation-related and general questions).
We find that, unlike mature fields like mobile development, there
is a contrast in the types of questions asked by developers. These
indicate a shallow understanding of the underlying technology that
empower such systems. We discuss several implications of these
findings via the lens of learning taxonomies to suggest how the
software engineering community can improve these services and
comment on the nature by which developers use them.