TY  - GEN
N1  - This version is the author accepted manuscript. For information on re-use, please refer to the publisher?s terms and conditions.
TI  - Customer Rating Reactions Can Be Predicted Purely Using App Features
AV  - public
SP  - 76
Y1  - 2018/10/15/
EP  - 87
CY  - Banff, Alberta, Canada
A1  - Sarro, F
A1  - Harman, M
A1  - Jia, Y
A1  - Zhang, Y
KW  - App Store Analysis
KW  -  Requirements Elicitation
KW  -  App Features Extraction
KW  - 
KW  -  Rating Estimation
KW  -  Mobile Applications
KW  -  Software Analytics
KW  -  Predictive Modelling
KW  - 
KW  -  Natural Language Processing
KW  -  Machine Learning
KW  -  Case Based Reasoning
N2  - 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.
ID  - discovery10057647
PB  - IEEE
UR  - https://doi.org/10.1109/RE.2018.00018
SN  - 2332-6441
ER  -