UBC Theses and Dissertations
Mining and characterizing cross-platform apps Ali, Mohamed Hassanein Tawfik Ibrahim
Smartphones and the applications (apps), which run on them, have grown tremendously over the past few years. To capitalize on this growth and attract more users, developing the same app for different platforms has become a common industry practice. However, each mobile platform has its own development language, Application program interfaces (APIs), software development kits (SDKs) and online stores for distributing apps to users. To understand the characteristics of and differences in how users perceive the same app implemented for and distributed through different platforms, we present a large-scale comparative study of cross-platform apps. We mine the characteristics of 80,000 app-pairs (160K apps in total) from a corpus of 2.4 million apps collected from the Apple and Google Play app stores. We quantitatively compare their app-store attributes, such as stars, versions, and prices. We measure the aggregated user-perceived ratings and find many differences across the platforms. Further, we employ machine learning to classify 1.7 million textual user reviews obtained from 2,000 of the mined app-pairs. We analyze discrepancies and root causes of user complaints to understand cross-platform development challenges that impact cross-platform user-perceived ratings. We also follow up with the developers to understand the reasons behind identified differences. Further, we take a closer look at a special category of cross-platform apps, which are built using Cross Platform Tools (CPTs). CPTs allow developers to use a common code-base to simultaneously create apps for multiple platforms. Apps created using these CPTs are called hybrid apps. We mine 15,512 hybrid apps; measure their aggregated user-perceived ratings and compare them to native apps of the same category.
Item Citations and Data
Attribution-NonCommercial-NoDerivatives 4.0 International