@inproceedings{discovery10057647,
           month = {October},
            year = {2018},
           title = {Customer Rating Reactions Can Be Predicted Purely Using App Features},
       publisher = {IEEE},
            note = {This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions.},
         address = {Banff, Alberta, Canada},
       booktitle = {Proceedings of the IEEE 26th International Requirements Engineering Conference :RE 18},
           pages = {76--87},
        keywords = {App Store Analysis, Requirements Elicitation, App Features Extraction,, Rating Estimation, Mobile Applications, Software Analytics, Predictive Modelling,, Natural Language Processing, Machine Learning, Case Based Reasoning},
             url = {https://doi.org/10.1109/RE.2018.00018},
          author = {Sarro, F and Harman, M and Jia, Y and Zhang, Y},
            issn = {2332-6441},
        abstract = {In this paper we provide empirical evidence that the rating that an app attracts can be accurately predicted from the features it offers. Our results, based on an analysis of 11,537 apps from the Samsung Android and BlackBerry World app stores, indicate that the rating of 89\% of these apps can be predicted with 100\% accuracy. Our prediction model is built by using feature and rating information from the existing apps offered in the App Store and it yields highly accurate rating predictions, using only a few (11-12) existing apps for case-based prediction. These findings may have important implications for require- ments engineering in app stores: They indicate that app devel- opers may be able to obtain (very accurate) assessments of the customer reaction to their proposed feature sets (requirements), thereby providing new opportunities to support the requirements elicitation process for app developers.}
}