  The online technical Q&A site Stack Overflow (SO) is popular among developers
to support their coding and diverse development needs. To address shortcomings
in API official documentation resources, several research has thus focused on
augmenting official API documentation with insights (e.g., code examples) from
SO. The techniques propose to add code examples/insights about APIs into its
official documentation. Reviews are opinionated sentences with
positive/negative sentiments. However, we are aware of no previous research
that attempts to automatically produce API documentation from SO by considering
both API code examples and reviews. In this paper, we present two novel
algorithms that can be used to automatically produce API documentation from SO
by combining code examples and reviews towards those examples. The first
algorithm is called statistical documentation, which shows the distribution of
positivity and negativity around the code examples of an API using different
metrics (e.g., star ratings). The second algorithm is called concept-based
documentation, which clusters similar and conceptually relevant usage
scenarios. An API usage scenario contains a code example, a textual description
of the underlying task addressed by the code example, and the reviews (i.e.,
opinions with positive and negative sentiments) from other developers towards
the code example. We deployed the algorithms in Opiner, a web-based platform to
aggregate information about APIs from online forums. We evaluated the
algorithms by mining all Java JSON-based posts in SO and by conducting three
user studies based on produced documentation from the posts.
