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App store mining has proven to be a promising technique for requirementselicitation as companies can gain valuable knowledge to maintain and evolveexisting apps. However, despite first advancements in using mining techniquesfor requirements elicitation, little is yet known how to distill requirementsfor new apps based on existing (similar) solutions and how exactlypractitioners would benefit from such a technique. In the proposed work, wefocus on exploring information (e.g. app store data) provided by the crowdabout existing solutions to identify key features of applications in aparticular domain. We argue that these discovered features and other relatedinfluential aspects (e.g. ratings) can help practitioners(e.g. softwaredeveloper) to identify potential key features for new applications. To supportthis argument, we first conducted an interview study with practitioners tounderstand the extent to which such an approach would find champions inpractice. In this paper, we present the first results of our ongoing researchin the context of a larger road-map. Our interview study confirms thatpractitioners see the need for our envisioned approach. Furthermore, we presentan early conceptual solution to discuss the feasibility of our approach.However, this manuscript is also intended to foster discussions on the extentto which machine learning can and should be applied to elicit automatedrequirements on crowd generated data on different forums and to identifyfurther collaborations in this endeavor.

Generating Requirements Out of Thin Air: Towards Automated Feature Identification for New Apps