Empowering developers with data-driven insights from mobile app ecosystem
Article
Ahasanuzzaman, M., Hassan, S., Bezemer, C. P., & Hassan, A. E. (2020). A longitudinal study of popular ad libraries in the Google Play Store. Empirical Software Engineering (EMSE), 25(1), 824-858.
In-app advertisements have become an integral part of the revenue model of mobile apps. To gain ad revenue, app developers integrate ad libraries into their apps. Such libraries are integrated to serve advertisements (ads) to users; developers then gain revenue based on the displayed ads and the users’ interactions with such ads. As a result, ad libraries have become an essential part of the mobile app ecosystem. However, little is known about how such ad libraries have evolved over time. In this paper, we study the evolution of the 8 most popular ad libraries (e.g., Google AdMob and Facebook Audience Network) over a period of 33 months (from April 2016 until December 2018). In particular, we look at their evolution in terms of size, the main drivers for releasing a new version, and their architecture. To identify popular ad libraries, we collect 35,462 updates of 1,840 top free-to-download apps in the Google Play Store. Then, we identify 63 ad libraries that are integrated into the studied popular apps. We observe that an ad library represents 10% of the binary size of mobile apps, and that the proportion of the ad library size compared to the app size has grown by 10% over our study period. By taking a closer look at the 8 most popular ad libraries, we find that ad libraries are continuously evolving with a median release interval of 34 days. In addition, we observe that some libraries have grown exponentially in size (e.g, Facebook Audience Network), while other libraries have attempted to reduce their size as they evolved. The libraries that reduced their size have done so through: (1) creating a lighter version of the ad library, (2) removing parts of the ad library, and (3) redesigning their architecture into a more modular one. To identify the main drivers for releasing a new version, we manually analyze the release notes of the eight studied ad libraries. We observe that fixing issues that are related to displaying video ads is the main driver for releasing new versions. We also observe that ad library developers are constantly updating their libraries to support a wider range of Android platforms (i.e., to ensure that more devices can use the libraries without errors). Finally, we derive a reference architecture from the studied eight ad libraries, and we study how these libraries deviated from this architecture in the study period. Our study is important for ad library developers as it provides the first in-depth look into how the important mobile app market segment of ad libraries has evolved. Our findings and the reference architecture are valuable for ad library developers who wish to learn about how other developers built and evolved their successful ad libraries. For example, our reference architecture provides a new ad library developer with a foundation for understanding the interactions between the most important components of an ad library.
Article
Ahasanuzzaman, M., Hassan, S., & Hassan, A. E. (2020). Studying ad library integration strategies of top free-to-download apps. IEEE Transactions on Software Engineering (TSE), 48(1), 209-224.
In-app advertisements have become a major revenue source for app developers in the mobile app ecosystem. Ad libraries play an integral part in this ecosystem as app developers integrate these libraries into their apps to display ads. In this paper, we study ad library integration strategies by analyzing 35,459 updates of 1,837 top free-to-download apps of the Google Play Store. We observe that ad libraries (e.g., Google AdMob) are not always used for serving ads – 22.5% of the apps that integrate Google AdMob do not display ads. They instead depend on Google AdMob for analytical purposes. Among the apps that display ads, we observe that 57.9% of them integrate multiple ad libraries. We observe that such integration of multiple ad libraries occurs commonly in apps with a large number of downloads and ones in app categories with a high proportion of ad-displaying apps. We manually analyze a sample of apps and derive a set of rules to automatically identify four common strategies for integrating multiple ad libraries. Our analysis of the apps across the identified strategies shows that app developers prefer to manage their own integrations instead of using off-the-shelf features of ad libraries for integrating multiple ad libraries. Our findings are valuable for ad library developers who wish to learn first hand about the challenges of integrating ad libraries
Article
Saidani, I., Ouni, A., Ahasanuzzaman, M., Hassan, S., Mkaouer, M. W., & Hassan, A. E. (2022). Tracking bad updates in mobile apps: A search-based approach. Empirical Software Engineering, 27(4), 81.
The rapid growth of the mobile applications development industry raises several new challenges to developers as they need to respond quickly to the users’ needs in a world of continuous changes. Indeed, mobile apps undergo frequent updates to introduce new features, fix reported issues or adapt to new technological or environment changes. Hence, introducing changes in this context is risky and can harmfully impact the application rating and competitiveness. Thus, ensuring that the application updates are deployed in a controlled way is of crucial importance. To better support mobile applications evolution and cut-off the costs of users dissatisfaction, we propose in this paper, AppTracker, a novel approach to automatically track bad release updates in Android applications (i.e., releases with higher percentage of negative reviews relative to the prior releases). We formulate the problem as a three-class classification problem to label the apps updates as bad, neutral or good. To solve this problem, we evolve bad release detection rules using Multi-Objective Genetic Programming (MOGP) based on the adaptation of the Non-dominated Sorting Genetic Algorithm (NSGA-II). In particular, the search process aims to provide the optimal trade-off between two conflicting objectives to deal with the considered classes. We evaluate our approach and investigate the performance of both within-project and cross-project validation scenarios on a benchmark of 50,700 updates from 1,717 free Android apps from Google Play Store. The statistical tests revealed that our approach achieves a clear advantage over machine learning approaches (e.g., random forest, decision tree, etc.) with significant improvements of 18% and 6% in terms of F1-score within-project and cross-project validations, respectively. Furthermore, the features analysis reveals that (1) the previous updates ratings and (2) the APK size are the most important features for both within and cross-project scenarios.