Details
| Original language | English |
|---|---|
| Title of host publication | 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW) |
| Editors | Tao Yue, Mehdi Mirakhorli |
| Publisher | IEEE Computer Society |
| Pages | 298-305 |
| Number of pages | 8 |
| ISBN (electronic) | 9781665418980 |
| Publication status | Published - 2021 |
| Event | 29th IEEE International Requirements Engineering Conference Workshops, REW 2021 - Virtual, Notre Dame, United States Duration: 20 Sept 2021 → 24 Sept 2021 Conference number: 29 |
Publication series
| Name | Proceedings of the IEEE International Conference on Requirements Engineering |
|---|---|
| Volume | 2021-September |
| ISSN (Print) | 1090-705X |
| ISSN (electronic) | 2332-6441 |
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.
Keywords
- classification, crowd, feedback, Requirements engineering, video comment, vision video
ASJC Scopus subject areas
- Computer Science(all)
- General Computer Science
- Engineering(all)
- General Engineering
- Business, Management and Accounting(all)
- Strategy and Management
Cite this
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2021 IEEE 29th International Requirements Engineering Conference Workshops (REW). ed. / Tao Yue; Mehdi Mirakhorli. IEEE Computer Society, 2021. p. 298-305 (Proceedings of the IEEE International Conference on Requirements Engineering; Vol. 2021-September).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
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TY - GEN
T1 - The Potential of Using Vision Videos for CrowdRE
T2 - 29th IEEE International Requirements Engineering Conference Workshops, REW 2021
AU - Karras, Oliver
AU - Kristo, Eklekta
AU - Klünder, Jil
N1 - Conference code: 29
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - classification
KW - crowd
KW - feedback
KW - Requirements engineering
KW - video comment
KW - vision video
UR - http://www.scopus.com/inward/record.url?scp=85118422812&partnerID=8YFLogxK
U2 - 10.15488/16379
DO - 10.15488/16379
M3 - Conference contribution
AN - SCOPUS:85118422812
T3 - Proceedings of the IEEE International Conference on Requirements Engineering
SP - 298
EP - 305
BT - 2021 IEEE 29th International Requirements Engineering Conference Workshops (REW)
A2 - Yue, Tao
A2 - Mirakhorli, Mehdi
PB - IEEE Computer Society
Y2 - 20 September 2021 through 24 September 2021
ER -