App Store Analysis studies information about applications obtained from app stores. App stores provide a wealth of information derived from users that would not exist had the applications been distributed via previous software deployment methods. App Store Analysis combines this non-technical information with technical information to learn trends and behaviours within these forms of software repositories. Findings from App Store Analysis have a direct and actionable impact on the software teams that develop software for app stores, and have led to techniques for requirements engineering, release planning, software design, security and testing. This survey describes and compares the areas of research that have been explored thus far, drawing out common aspects, trends and directions future research should take to address open problems and challenges.

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A Survey of App Store Analysis for Software Engineering

Third-party libraries on Android have been shown to be security and privacy hazards by adding security vulnerabilities to their host apps or by misusing inherited access rights. Correctly attributing improper app behavior either to app or library developer code or isolating library code from their host apps would be highly desirable to mitigate these problems, but is impeded by the absence of a third-party library detection that is effective and reliable in spite of obfuscated code. This paper proposes a library detection technique that is resilient against common code obfuscations and that is capable of pinpointing the exact library version used in apps. Libraries are detected with profiles from a comprehensive library database that we generated from the original library SDKs. We apply our technique to the top apps on Google Play and their complete histories to conduct a longitudinal study of library usage and evolution in apps. Our results particularly show that app developers only slowly adapt new library versions, exposing their end-users to large windows of vulnerability. For instance, we discovered that two long-known security vulnerabilities in popular libs are still present in the current top apps. Moreover, we find that misuse of cryptographic APIs in advertising libs, which increases the host apps' attack surface, affects 296 top apps with a cumulative install base of 3.7bn devices according to Play. To the best of our knowledge, our work is first to quantify the security impact of third-party libs on the Android ecosystem.

Reliable Third-Party Library Detection in Android and its Security Applications

Since ontology-driven software development (ODSD) is an integration of ontology technologies and model-driven software development (MDSD), it is necessary to identify and analyse technologies applied in MDSD. We define basic concepts, such as model-driven engineering, metamodelling, model transformation and technological space, and describe the state-of-the-art implementations of these concepts.

Model-Driven Software Development

Mobile applications (apps) have been gaining rising popularity due to the advances in mobile technologies and the large increase in the number of mobile users. Consequently, several app distribution platforms, which provide a new way for developing, downloading, and updating software applications in modern mobile devices, have recently emerged. To better understand the download patterns, popularity trends, and development strategies in this rapidly evolving mobile app ecosystem, we systematically monitored and analyzed four popular third-party Android app marketplaces. Our study focuses on measuring, analyzing, and modeling the app popularity distribution, and explores how pricing and revenue strategies affect app popularity and developers' income. Our results indicate that unlike web and peer-to-peer file sharing workloads, the app popularity distribution deviates from commonly observed Zipf-like models. We verify that these deviations can be mainly attributed to a new download pattern, to which we refer as the clustering effect. We validate the existence of this effect by revealing a strong temporal affinity of user downloads to app categories. Based on these observations, we propose a new formal clustering model for the distribution of app downloads, and demonstrate that it closely fits measured data. Moreover, we observe that paid apps follow a different popularity distribution than free apps, and show how free apps with an ad-based revenue strategy may result in higher financial benefits than paid apps. We believe that this study can be useful to appstore designers for improving content delivery and recommendation systems, as well as to app developers for selecting proper pricing policies to increase their income.

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Rise of the planet of the apps: a systematic study of the mobile app ecosystem

Mining smartphone data for app usage prediction and recommendations: A survey

Distributions of popularity of many online markets have long tails. However, the profitability of the long tail remains in dispute among researchers. With an analysis of an extensive dataset of Google Play transactions, this work first examine the long tail of the mobile application market. Our results suggest that Google Play is more of a "Superstar" market strongly dominated by popular hit products than a "Long-tail" market where unpopular niche products aggregately contribute to a substantial portion of popularity. Blockbuster apps have more downloads, higher ratings and satisfaction ratio. Additionally, we investigate the impact of price on sales of paid apps and find that certain expensive professional apps constitute disproportional large sales. Our findings reveal the unique market structure of the mobile app market, under which the discovery of niche apps is still an intractable task.

Google play is not a long tail market: an empirical analysis of app adoption on the Google play app market

Many online markets are found with a long tail in sales distribution. With the analysis of a large data set of transactions in Android Market, this work first brings the examination of long tail to the mobile application market. The results suggest that, rather than being a “Long Tail” market where unpopular niche products aggregately contribute to substantial portion of sales, the Android Market is more a “Superstar” market strongly dominated by popular hit products. Hit apps are also found to have higher user consumption and satisfaction rate. Besides, we investigate the impact of price and finds that some expensive apps constitute unproportional large sales. Our findings reveal possible different market structure of mobile app market and point out challenges to app developers. Author Keywords

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Long Tail or Superstar? An analysis of app adoption on the Android Market

This talk presents the examination of the long tail of sales distribution in mobile app market. With the analysis of a large data set of transactions in Android Market, our research suggests that, rather than being a "Long Tail" market where unpopular niche products aggregately contribute to substantial portion of sales, the Android Market is more a "Superstar" market strongly dominated by popular hit products. We also show that though most downloads of paid apps are from cheap apps, but some expensive apps accounts for unproportional large revenue.

Nan Zhong

Papers

Google play is not a long tail market: an empirical analysis of app adoption on the Google play app market

Long Tail or Superstar? An analysis of app adoption on the Android Market

Where should you focus: Long Tail or Superstar?: an analysis of app adoption on the Android Market