Mining App Stores: Extracting Technical, Business and Customer Rating Information for Analysis and Prediction

This paper formulates app store analysis as an instance of software repository mining. We use data mining to extract feature information, together with more readily available price and popularity information, to support analysis that combines technical, business and customer facing app store properties. We applied our approach to 32,108 non-zero priced apps available from the Blackberry app store. Our results show that there is a strong correlation between customer rating and the rank of app downloads, though perhaps surprisingly, there is no correlation between price and downloads, nor between price and rating. We provide empirical evidence that our extracted features are meaningful and valuable: they maintain correlations observed at the app level and provide the input to price prediction system that we construct using Case Based Reasoning. Our prediction system statistically significantly outperforms recommended existing approaches to price estimation (and with at least medium effect size) in 16 out of 17 of Blackberry App Store categories. UCL DEPARTMENT OF COMPUTER SCIENCE