App store mining and analysis: MSR for app stores

This paper introduces app store mining and analysis as a form of software repository mining. Unlike other software repositories traditionally used in MSR work, app stores usually do not provide source code. However, they do provide a wealth of other information in the form of pricing and customer reviews. Therefore, we use data mining to extract feature information, which we then combine with more readily available information to analyse apps' technical, customer and business aspects. We applied our approach to the 32,108 non-zero priced apps available in the Blackberry app store in September 2011. 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. More importantly, we show that these correlation findings carry over to (and are even occasionally enhanced within) the space of data mined app features, providing evidence that our `App store MSR' approach can be valuable to app developers.

I. Introduction

App stores provide a rich source of information about apps concerning their customer-, business- and technically-focussed attributes. Customer information is available concerning the ratings accorded to apps by the users who have downloaded them. This provides both qualitative and quantitative data about the customer perception of the apps. Business information is available concerning the downloads and price of apps. Technical information is also available in the descriptions of apps, but it is in free text format, so data mining is required to extract the technical details required for analysis.