ABSTRACT
App marketplaces, i.e. Google Play Store and Apple AppStore,comprise many competing apps offering a fair set of similar features. Users of competing apps can submit feedback in the form of ratings and textual comments. The feedback is useful for app developers to get a better understanding of where their app stands in the competition. However, app ratings don’t provide concrete information regarding users’ perceptions about an app’s features compared to similar apps. Studies have shown that users express sentiments on app features in app reviews. Therefore, user reviews are a valuable information source to compare competing apps based on users' sentiments regarding features. So far, researchers have analyzed app reviews to summarize the users’ sentiments on app features but the existing approaches have not been used for the comparison of competing apps. In this direction, we analyze app reviews of 25 apps to extract app features, determine competing apps based on feature commonality, and then compare competing apps based on users’ sentiments regarding features. We developed a tool prototype that helps app developers in identifying features which have been perceived negatively by its users. The tool prototype is also useful to find a set of features loved by users in other similar apps but missing in one’s app. We demonstrate the usefulness of the tool prototype and give pointers to future work.
- Al-Subaihin, A. et al. 2016. Clustering Mobile Apps Based on Mined Textual Features. 10th International Conference on Empirical Software Engineering and Measurement (2016). Google ScholarDigital Library
- Chen, N. et al. 2014. AR-miner: mining informative reviews for developers from mobile app marketplace. Proceedings of the 36th International Conference on Software Engineering - ICSE 2014 (New York, New York, USA, 2014), 767–778. Google ScholarDigital Library
- Finkelstein, A. et al. 2014. App Store Analysis : Mining App Stores for Relationships between Customer, Business and Technical Characteristics. UCL Research Note. 14/10, (2014), 1–24.Google Scholar
- Garcia, D. et al. 2013. The Role of Emotions in Contributors Activity: A Case Study on the GENTOO Community. 2013 International Conference on Cloud and Green Computing (Sep. 2013), 410–417. Google ScholarDigital Library
- Gu, X. and Kim, S. 2015. What parts of your apps are loved by users? Proceedings - 2015 30th IEEE/ACM International Conference on Automated Software Engineering, ASE 2015 (Nov. 2015), 760–770.Google Scholar
- Guzman, E. and Maalej, W. 2014. How Do Users Like This Feature? A Fine Grained Sentiment Analysis of App Reviews. 2014 IEEE 22nd International Requirements Engineering Conference (RE) (Aug. 2014), 153–162.Google ScholarCross Ref
- Jongeling, R. et al. 2015. Choosing your weapons: On sentiment analysis tools for software engineering research. 2015 IEEE International Conference on Software Maintenance and Evolution (ICSME) (Sep. 2015), 531–535. Google ScholarDigital Library
- Keertipati, S. et al. 2016. Approaches for prioritizing feature improvements extracted from app reviews. EASE ’16 Proceedings of the 20th International Conference on Evaluation and Assessment in Software Engineering. (2016). Google ScholarDigital Library
- Ortu, M. et al. 2015. Are Bullies More Productive? Empirical Study of Affectiveness vs. Issue Fixing Time. 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories (May 2015), 303–313. Google ScholarDigital Library
- Pletea, D. et al. 2014. Security and emotion: sentiment analysis of security discussions on GitHub. Proceedings of the 11th Working Conference on Mining Software Repositories - MSR 2014 (New York, New York, USA, 2014), 348–351. Google ScholarDigital Library
- Thelwall, M. et al. 2010. Sentiment strength detection in short informal text. Journal of the American Society for Information Science and Technology. 61, 12 (Dec. 2010), 2544–2558. Google ScholarDigital Library
- Villarroel, L. et al. 2016. Release planning of mobile apps based on user reviews. Proceedings of the 38th International Conference on Software Engineering - ICSE ’16 (New York, New York, USA, 2016), 14–24. Google ScholarDigital Library
Index Terms
- Feature-based evaluation of competing apps
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