  Using cloud-based computer vision services is gaining traction, where
developers access AI-powered components through familiar RESTful APIs, not
needing to orchestrate large training and inference infrastructures or
curate/label training datasets. However, while these APIs seem familiar to use,
their non-deterministic run-time behaviour and evolution is not adequately
communicated to developers. Therefore, improving these services' API
documentation is paramount-more extensive documentation facilitates the
development process of intelligent software. In a prior study, we extracted 34
API documentation artefacts from 21 seminal works, devising a taxonomy of five
key requirements to produce quality API documentation. We extend this study in
two ways. Firstly, by surveying 104 developers of varying experience to
understand what API documentation artefacts are of most value to practitioners.
Secondly, identifying which of these highly-valued artefacts are or are not
well-documented through a case study in the emerging computer vision service
domain. We identify: (i) several gaps in the software engineering literature,
where aspects of API documentation understanding is/is not extensively
investigated; and (ii) where industry vendors (in contrast) document artefacts
to better serve their end-developers. We provide a set of recommendations to
enhance intelligent software documentation for both vendors and the wider
research community.
