Mining Android apps to predict market ratings

Market rating systems give Android users the opportunity to provide feedback on an application (app). Developers aspire for the highest ratings possible, as they reflect upon user perceptions of their apps. However, no mechanism exists to predict in any way the market rating of an app before publication. We downloaded and reverse-engineered 10,740 apps from the Slide Me market, and analyzed them using quality related metrics. We compared the results of the 1,000 highest rated apps against the lowest rated 1,000. Our results show that traditional white box quality metrics do little to distinguish the groups, while certain Android specific user-perspective metrics are useful in prediction.