  App store mining has proven to be a promising technique for requirements
elicitation as companies can gain valuable knowledge to maintain and evolve
existing apps. However, despite first advancements in using mining techniques
for requirements elicitation, little is yet known how to distill requirements
for new apps based on existing (similar) solutions and how exactly
practitioners would benefit from such a technique. In the proposed work, we
focus on exploring information (e.g. app store data) provided by the crowd
about existing solutions to identify key features of applications in a
particular domain. We argue that these discovered features and other related
influential aspects (e.g. ratings) can help practitioners(e.g. software
developer) to identify potential key features for new applications. To support
this argument, we first conducted an interview study with practitioners to
understand the extent to which such an approach would find champions in
practice. In this paper, we present the first results of our ongoing research
in the context of a larger road-map. Our interview study confirms that
practitioners see the need for our envisioned approach. Furthermore, we present
an early conceptual solution to discuss the feasibility of our approach.
However, this manuscript is also intended to foster discussions on the extent
to which machine learning can and should be applied to elicit automated
requirements on crowd generated data on different forums and to identify
further collaborations in this endeavor.
