Abstract
[Context and motivation] App stores and social media channels such as Twitter enable users to share feedback regarding software. Due to its high volume, it is hard to effectively and systematically process such feedback to obtain a good understanding of users’ opinions about a software product. [Question/problem] Tools based on natural language processing and machine learning have been proposed as an inexpensive mechanism for classifying user feedback. Unfortunately, the accuracy of these tools is imperfect, which jeopardizes the reliability of the analysis results. We investigate whether assigning micro-tasks to crowd workers could be an alternative technique for identifying and classifying requirements in user feedback. [Principal ideas/results] We present a crowdsourcing method for filtering out irrelevant app store reviews and for identifying features and qualities. A validation study has shown positive results in terms of feasibility, accuracy, and cost. [Contribution] We provide evidence that crowd workers can be an inexpensive yet accurate resource for classifying user reviews. Our findings contribute to the debate on the roles of and synergies between humans and AI techniques.
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Notes
- 1.
In this paper, user requirements are understood as “a need perceived by a stakeholder”, as per one sub-definition of requirement in the IREB Glossary [9].
- 2.
Kyōryoku
is a Japanese term for collaboration: literally, it combines strength
with cooperation
. - 3.
- 4.
Note: because Phases 1 and 2 focus on filtering out irrelevant reviews, we take the useless category as our positives.
is a Japanese term for collaboration: literally, it combines strength
with cooperation
.References
Castro-Herrera, C., Duan, C., Cleland-Huang, J., Mobasher, B.: Using data mining and recommender systems to facilitate large-scale, open, and inclusive requirements elicitation processes. In: Proceedings of the RE, pp. 165–168 (2008)
Chandy, R., Gu, H.: Identifying spam in the iOS app store. In: Proceedings of the WebQuality, pp. 56–59 (2012)
Chen, N., Lin, J., Hoi, S.C., Xiao, X., Zhang, B.: AR-miner: mining informative reviews for developers from mobile app marketplace. In: Proceedings of the ICSE, pp. 767–778 (2014)
Dalpiaz, F., Parente, M.: RE-SWOT: from user feedback to requirements via competitor analysis. In: Knauss, E., Goedicke, M. (eds.) REFSQ 2019. LNCS, vol. 11412, pp. 55–70. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-15538-4_4
Dhinakaran, V.T., Pulle, R., Ajmeri, N., Murukannaiah, P.K.: App review analysis via active learning. In: Proceedings of the RE, pp. 170–181 (2018)
Fort, K., Adda, G., Cohen, K.B.: Amazon mechanical turk: gold mine or coal mine? Comput. Linguist. 37(2), 413–420 (2011)
Gadiraju, U., Fetahu, B., Kawase, R.: Training workers for improving performance in crowdsourcing microtasks. In: Conole, G., Klobučar, T., Rensing, C., Konert, J., Lavoué, É. (eds.) EC-TEL 2015. LNCS, vol. 9307, pp. 100–114. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24258-3_8
Glinz, M.: On non-functional requirements. In: Proceedings of the RE, pp. 21–26 (2007)
Glinz, M.: A glossary of requirements engineering terminology. Version 1.7. International Requirements Engineering Board (IREB) (2017). https://www.ireb.org/en/cpre/cpre-glossary/
Glinz, M.: CrowdRE: achievements, opportunities and pitfalls. In: Proceedings of the CrowdRE, pp. 172–173 (2019)
Groen, E.C., Kopczyńska, S., Hauer, M.P., Krafft, T.D., Doerr, J.: Users—The hidden software product quality experts? A study on how app users report quality aspects in online reviews. In: Proceedings of the RE, pp. 80–89 (2017)
Groen, E.C., Schowalter, J., Kocpzyńska, S., Polst, S., Alvani, S.: Is there really a need for using NLP to elicit requirements? A benchmarking study to assess scalability of manual analysis. In: Proceedings of the NLP4RE (2018)
Groen, E.C., et al.: The crowd in requirements engineering: the landscape and challenges. IEEE Softw. 34(2), 44–52 (2017)
Guzman, E., Maalej, W.: How do users like this feature? A fine grained sentiment analysis of app reviews. In: Proceedings of the RE, pp. 153–162 (2014)
Horton, J.J., Chilton, L.B.: The labor economics of paid crowdsourcing. In: Proceedings of the EC, pp. 209–218 (2010)
Hosseini, M., Groen, E.C., Shahri, A., Ali, R.: CRAFT: a crowd-annotated feedback technique. In: Proceedings of the CrowdRE, pp. 170–175 (2017)
Hosseini, M., Phalp, K.T., Taylor, J., Ali, R.: Towards crowdsourcing for requirements engineering. In: Proceedings of REFSQ Workshops (2014)
Howe, J.: The rise of crowdsourcing. Wired 14(6), 1–4 (2006)
Kittur, A., Smus, B., Khamkar, S., Kraut, R.E.: CrowdForge: crowdsourcing complex work. In: Proceedings of the UIST, pp. 43–52 (2011)
Lim, S.L., Finkelstein, A.: StakeRare: using social networks and collaborative filtering for large-scale requirements elicitation. IEEE Trans. Softw. Eng. 38(3), 707–735 (2012)
Lu, M., Liang, P.: Automatic classification of non-functional requirements from augmented app user reviews. In: Proceedings of the EASE, pp. 344–353 (2017)
Maalej, W., Nabil, H.: Bug report, feature request, or simply praise? On automatically classifying app reviews. In: Proceedings of the RE, pp. 116–125 (2015)
Nayebi, M., Cho, H., Ruhe, G.: App store mining is not enough for app improvement. Empir. Softw. Eng. 23(5), 2764–2794 (2018). https://doi.org/10.1007/s10664-018-9601-1
Panichella, S., Di Sorbo, A., Guzman, E., Visaggio, C.A., Canfora, G., Gall, H.C.: How can I improve my app? Classifying user reviews for software maintenance and evolution. In: Proceedings of the ICSME, pp. 281–290 (2015)
Posch, L., Bleier, A., Flöck, F., Strohmaier, M.: Characterizing the global crowd workforce: a cross-country comparison of crowdworker demographics. arXiv preprint arXiv:1812.05948 (2018)
Renzel, D., Behrendt, M., Klamma, R., Jarke, M.: Requirements bazaar: social requirements engineering for community-driven innovation. In: Proceedings of the RE, pp. 326–327 (2013)
Retelny, D., et al.: Expert crowdsourcing with flash teams. In: Proceedings of the UIST, pp. 75–85 (2014)
Schenk, E., Guittard, C.: Towards a characterization of crowdsourcing practices. J. Innov. Econ. Manag. 1(7), 93–107 (2011)
Snijders, R., Dalpiaz, F., Brinkkemper, S., Hosseini, M., Ali, R., Ozum, A.: REfine: a gamified platform for participatory requirements engineering. In: Proceedings of the CrowdRE, pp. 1–6 (2015)
Stanik, C., Haering, M., Maalej, W.: Classifying multilingual user feedback using traditional machine learning and deep learning. In: Proceedings of the AIRE (2019)
Stol, K.J., Fitzgerald, B.: Two’s company, three’s a crowd: a case study of crowdsourcing software development. In: Proceedings of the ICSE, pp. 187–198 (2014)
Valentine, M.A., Retelny, D., To, A., Rahmati, N., Doshi, T., Bernstein, M.S.: Flash organizations: crowdsourcing complex work by structuring crowds as organizations. In: Proceedings of the CHI, pp. 3523–3537 (2017)
van Vliet, M., Groen, E., Dalpiaz, F., Brinkkemper, S.: Crowd-annotation results: identifying and classifying user requirements in online feedback (2020). https://doi.org/10.23644/uu.c.4815591.v1. Zenodo
Williams, G., Mahmoud, A.: Mining Twitter feeds for software user requirements. In: Proceedings of the RE, pp. 1–10 (2017)
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van Vliet, M., Groen, E.C., Dalpiaz, F., Brinkkemper, S. (2020). Identifying and Classifying User Requirements in Online Feedback via Crowdsourcing. In: Madhavji, N., Pasquale, L., Ferrari, A., Gnesi, S. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2020. Lecture Notes in Computer Science(), vol 12045. Springer, Cham. https://doi.org/10.1007/978-3-030-44429-7_11
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