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Publication Detail
Customer Rating Reactions Can Be Predicted Purely Using App Features
  • Publication Type:
  • Authors:
    Sarro F, Harman M, Jia Y, Zhang Y
  • Publisher:
  • Publication date:
  • Published proceedings:
    2018 IEEE 26th International Requirements Engineering Conference (RE)
  • Status:
  • Name of conference:
    IEEE 26th International Requirements Engineering Conference (RE)
  • Conference place:
    Banff, Canada
  • Conference start date:
  • Conference finish date:
  • 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
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.
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