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Generating requirements out of thin air: Towards automated feature identification for new apps
Fortiss GmbH, DEU.
Fachhochschule Nordwestschweiz FHNW, DEU.
Blekinge Institute of Technology, Faculty of Computing, Department of Software Engineering.
2019 (English)In: Proceedings - 2019 IEEE 27th International Requirements Engineering Conference Workshops, REW 2019, Institute of Electrical and Electronics Engineers Inc. , 2019, p. 193-199, article id 8933543Conference paper, Published paper (Refereed)
Abstract [en]

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. © 2019 IEEE.

Place, publisher, year, edition, pages
Institute of Electrical and Electronics Engineers Inc. , 2019. p. 193-199, article id 8933543
Keywords [en]
App store mining, Crowd data, Machine learning, Software feature mapping, E-learning, Learning systems, Requirements engineering, App stores, Automated features, Mining techniques, New applications, Requirements elicitation, Software developer, Software features, Application programs
National Category
Software Engineering
Identifiers
URN: urn:nbn:se:bth-19177DOI: 10.1109/REW.2019.00040Scopus ID: 2-s2.0-85078017186ISBN: 9781728151656 (print)OAI: oai:DiVA.org:bth-19177DiVA, id: diva2:1391984
Conference
27th IEEE International Requirements Engineering Conference Workshops, REW; Jeju Island; South Korea, 23 September 2019 through 27 September 2019
Available from: 2020-02-06 Created: 2020-02-06 Last updated: 2020-02-21Bibliographically approved

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Mendez, Daniel

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