The DOI matches its Crossref record; 37 of the 37 references it cites were found in a registry or an index.
AR-miner: mining informative reviews for developers from mobile app marketplace
Open accessAbstract
With the popularity of smartphones and mobile devices, mobile application (a.k.a. “app”) markets have been growing exponentially in terms of number of users and downloads. App developers spend considerable effort on collecting and exploiting user feedback to improve user satisfaction, but suffer from the absence of effective user review analytics tools. To facilitate mobile app developers discover the most “informative” user reviews from a large and rapidly increasing pool of user reviews, we present “AR-Miner” — a novel computational framework for App Review Mining, which performs comprehensive analytics from raw user reviews by (i) first extracting informative user reviews by filtering noisy and irrelevant ones, (ii) then grouping the informative reviews automatically using topic modeling, (iii) further prioritizing the informative reviews by an effective review ranking scheme, (iv) and finally presenting the groups of most “informative” reviews via an intuitive visualization approach. We conduct extensive experiments and case studies on four popular Android apps to evaluate AR-Miner, from which the encouraging results indicate that AR-Miner is effective, efficient and promising for app developers.
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How can i improve my app? Classifying user reviews for software maintenance and evolution
Panichella, S., Di Sorbo, A., Guzman, E., Visaggio, C. A., Canfora, G., & Gall, H. C. (2015) · 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME) · doi:10.1109/icsm.2015.7332474 · Cited by 455
What would users change in my app? summarizing app reviews for recommending software changes
Di Sorbo, A., Panichella, S., Alexandru, C. V., Shimagaki, J., Visaggio, C. A., Canfora, G., & Gall, H. C. (2016) · Proceedings of the 2016 24th ACM SIGSOFT International Symposium on Foundations of Software Engineering · doi:10.1145/2950290.2950299 · Cited by 294
Ensemble Methods for App Review Classification: An Approach for Software Evolution (N)
Guzman, E., El-Haliby, M., & Bruegge, B. (2015) · 2015 30th IEEE/ACM International Conference on Automated Software Engineering (ASE) · doi:10.1109/ase.2015.88 · Cited by 143
A Survey of Performance Optimization for Mobile Applications
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Metadata
- Authors
- Chen, N., Lin, J., Hoi, S. C. H., Xiao, X., & Zhang, B.
- Venue
- Proceedings of the 36th International Conference on Software Engineering
- Publisher
- ACM
- Published
- 2014-05-31
- Pages
- 767-778
- ISSN
- 0270-5257 (ISSN-L)
- OpenAlex
- W2112143630
- Cited by
- 517
- References
- 37
- FWCI
- 27.27
- Type
- conference paper
- Language
- en
- Open access
- green
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- Chen, N., Lin, J., Hoi, S. C. H., Xiao, X., & Zhang, B. (2014). AR-miner: mining informative reviews for developers from mobile app marketplace. Proceedings of the 36th International Conference on Software Engineering. https://doi.org/10.1145/2568225.2568263
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