The Potential of Using Vision Videos for CrowdRE: Video Comments as a Source of Feedback

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dc.identifier.uri http://dx.doi.org/10.15488/16379
dc.identifier.uri https://www.repo.uni-hannover.de/handle/123456789/16506
dc.contributor.author Karras, Oliver
dc.contributor.author Kristo, Eklekta
dc.contributor.author Klünder, Jil
dc.date.accessioned 2024-02-26T09:35:46Z
dc.date.available 2024-02-26T09:35:46Z
dc.date.issued 2021
dc.identifier.citation Karras, O.; Kristo, E.; Klünder, J.: The Potential of Using Vision Videos for CrowdRE: Video Comments as a Source of Feedback. In: 29th IEEE International Requirements Engineering Conference workshops : REW 2021 : September 20-24 2021, online event : proceedings. Piscataway, NJ : IEEE, 2021, S. 298-305. DOI: https://doi.org/10.1109/rew53955.2021.00053
dc.description.abstract Vision videos are established for soliciting feedback and stimulating discussions in requirements engineering (RE) practices such as focus groups. Different researchers motivated the transfer of these benefits into crowd-based RE (CrowdRE) by using vision videos on social media platforms. So far, however, little research explored the potential of using vision videos for CrowdRE in detail. In this paper, we analyze and assess this potential, in particular, focusing on video comments as a source of feedback. In a case study, we analyzed 4505 comments on a vision video from YouTube. We found that the video solicited 2770 comments from 2660 viewers in four days. This is more than 50% of all comments the video received in four years. Even though only a certain fraction of these comments are relevant to RE, the relevant comments address typical intentions and topics of user feedback, such as feature request or problem report. Besides the typical user feedback categories, we found more than 300 comments that address the topic safety which has not appeared in previous analyses of user feedback. In an automated analysis, we compared the performance of three machine learning algorithms on classifying the video comments. Despite certain differences, the algorithms classified the video comments well. Based on these findings, we conclude that the use of vision videos for CrowdRE has a large potential. Despite the preliminary nature of the case study, we are optimistic that vision videos can motivate stakeholders to actively participate in a crowd and solicit numerous of video comments as a valuable source of feedback.© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. eng
dc.language.iso eng
dc.publisher Piscataway, NJ : IEEE
dc.rights Es gilt deutsches Urheberrecht. Das Dokument darf zum eigenen Gebrauch kostenfrei genutzt, aber nicht im Internet bereitgestellt oder an Außenstehende weitergegeben werden.
dc.subject classification eng
dc.subject crowd eng
dc.subject feedback eng
dc.subject Requirements engineering eng
dc.subject video comment eng
dc.subject vision video eng
dc.subject.classification Konferenzschrift ger
dc.subject.ddc 004 | Informatik
dc.title The Potential of Using Vision Videos for CrowdRE: Video Comments as a Source of Feedback eng
dc.type BookPart
dc.type Text
dc.relation.isbn 978-1-6654-1898-0
dc.relation.doi https://doi.org/10.1109/rew53955.2021.00053
dc.bibliographicCitation.firstPage 298
dc.bibliographicCitation.lastPage 305
dc.description.version acceptedVersion eng
tib.accessRights frei zug�nglich


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