Abstract
[Context and motivation] In the increasingly competitive software market, it is essential for software companies to have a comprehensive understanding of development progress and user preferences of their corresponding application domain. [Question/problem] However, given the huge number of existing software applications, it is impossible to gain such insights via manual inspection. [Principal ideas/results] In this paper, we present a research preview of automatic user preferences elicitation approach. Specifically, our approach first clusters software applications into different categories based on their descriptions, and then identifies features of each category. We then link such features to corresponding user reviews and automatically classify sentiments of each review In order to understand user preferences over such feature In addition, we have carefully planned evaluations that will be carried out to further polish our work. [Contributions] Our proposal aims to help software companies to identify features of applications in a particular domain, as well as user preferences with regard to those features. We argue such analysis is especially important for startup companies that have few knowledge about the domain.
Keywords
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsReferences
Maalej, W., Nayebi, M., Johann, T., Ruhe, G.: Toward data-driven requirements engineering. IEEE Softw. 33(1), 48–54 (2016)
Guzman, E., Maalej, W.: How do users like this feature? a fine grained sentiment analysis of app reviews. In: 2014 IEEE 22nd International Requirements Engineering Conference (RE), pp. 153–162, August 2014
Chen, N., Lin, J., Hoi, S.C.H., Xiao, X., Zhang, B.: AR-miner: Mining informative reviews for developers from mobile app marketplace. In: Proceedings of the 36th International Conference on Software Engineering, ICSE 2014, pp. 767–778. ACM, New York (2014)
Carreo, L.V.G., Winbladh, K.: Analysis of user comments: an approach for software requirements evolution. In: 2013 35th International Conference on Software Engineering (ICSE), pp. 582–591, May 2013
Hariri, N., Castro-Herrera, C., Mirakhorli, M., Cleland-Huang, J., Mobasher, B.: Supporting domain analysis through mining and recommending features from online product listings. IEEE Trans. Softw. Eng. 39(12), 1736–1752 (2013)
Zou, Y., Liu, C., Jin, Y., Xie, B.: Assessing software quality through web comment search and analysis. In: Favaro, J., Morisio, M. (eds.) ICSR 2013. LNCS, vol. 7925, pp. 208–223. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-38977-1_14
Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Le, Q., Mikolov, T.: Distributed representations of sentences and documents. In: Proceedings of the 31st International Conference on Machine Learning (ICML 2014), pp. 1188–1196 (2014)
Lau, J.H., Baldwin, T.: An empirical evaluation of doc2vec with practical insights into document embedding generation arXiv preprint arXiv:1607.05368. (2016)
Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)
Manning, C.D., Schütze, H., et al.: Foundations of Statistical Natural Language Processing, vol. 999. MIT Press, Cambridge (1999)
Mikolov, T., Chen, K., Corrado, G., Dean, J.: Efficient estimation of word representations in vector space arXiv preprint arXiv:1301.3781. (2013)
Panichella, S., Di Sorbo, A., Guzman, E., Visaggio, C.A., Canfora, G., Gall, H.C.: ARdoc: App reviews development oriented classifier. In: Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering, pp. 1023–1027. ACM (2016)
Baeza-Yates, R., Ribeiro-Neto, B., et al.: Modern Information Retrieval, vol. 463. ACM Press, New York (1999)
Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Q. 13(3), 319–340 (1989)
Acknowledgements
The work is supported by National Natural Science Foundation of China (Grants No. 91546111 and No. 91646201), Beijing Natural Science Foundation P.R.China (Grants No. 4173072), and Foundation of Beijing University of Technology.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Li, T., Zhang, F., Wang, D. (2018). Automatic User Preferences Elicitation: A Data-Driven Approach. In: Kamsties, E., Horkoff, J., Dalpiaz, F. (eds) Requirements Engineering: Foundation for Software Quality. REFSQ 2018. Lecture Notes in Computer Science(), vol 10753. Springer, Cham. https://doi.org/10.1007/978-3-319-77243-1_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-77243-1_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-77242-4
Online ISBN: 978-3-319-77243-1
eBook Packages: Computer ScienceComputer Science (R0)