  Code example is a crucial part of good documentation. It helps the developers
to understand the documentation easily and use the corresponding code unit
(e.g., method) properly. However, many official documentation still lacks
(good) code example and it is one of the common documentation issues as found
by several studies. Hence in this paper, we consider automatic code example
generation for documentation, a direction less explored by the existing
research. We employ Codex, a GPT-3 based model, pre-trained on both natural and
programming languages to generate code examples from source code and
documentation given as input. Our preliminary investigation on 40 scikit-learn
methods reveals that this approach is able to generate good code examples where
72.5% code examples were executed without error (passability) and 82.5%
properly dealt with the target method and documentation (relevance). We also
find that incorporation of error logs (produced by the compiler while executing
a failed code example) in the input further improves the passability from 72.5%
to 87.5%. Thus, our investigation sets the base of documentation-specific code
example generation and warrants in-depth future studies.
