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.
- Code Documentation
- Computer Vision
- Intelligent Web Services and Semantic Web