Comprehension of Ads-supported and Paid Android Applications: Are They Different?

The Android market is a place where developers offer paid and-or free apps to users. Free apps are interesting to users because they can try them immediately without incurring a monetary cost. However, free apps often have limited features and-or contain ads when compared to their paid counterparts. Thus, users may eventually need to pay to get additional features and-or remove ads. While paid apps have clear market values, their ads-supported versions are not entirely free because ads have an impact on performance. In this paper, first, we perform an exploratory study about ads-supported and paid apps to understand their differences in terms of implementation and development process. We analyze 40 Android apps and we observe that (i) ads-supported apps are preferred by users although paid apps have a better rating, (ii) developers do not usually offer a paid app without a corresponding free version, (iii) ads-supported apps usually have more releases and are released more often than their corresponding paid versions, (iv) there is no a clear strategy about the way developers set prices of paid apps, (v) paid apps do not usually include more functionalities than their corresponding ads-supported versions, (vi) developers do not always remove ad networks in paid versions of their ads-supported apps, and (vii) paid apps require less permissions than ads-supported apps. Second, we carry out an experimental study to compare the performance of ads-supported and paid apps and we propose four equations to estimate the cost of ads-supported apps. We obtain that (i) ads-supported apps use more resources than their corresponding paid versions with statistically significant differences and (ii) paid apps could be considered a most cost-effective choice for users because their cost can be amortized in a short period of time, depending on their usage.Comment: Accepted for publication in the proceedings of the IEEE International Conference on Program Comprehension 201

A survey of the role of viewability within the online advertising ecosystem

© 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes,creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.Within the online advertising ecosystem, viewability is defined as the metric that measures if an ad impression had the chance of being viewable by a potential consumer. Although this metric has been presented as a potential game-changer within the ad industry, it has not been fully adopted by the stakeholders, mainly due to disagreement between the different parties on the standards to implement and measure it, and its potential benefits and drawbacks. In this study, we present a survey of the role that viewability can have on the main challenges of the online advertising ecosystem depicting the main applications, benefits and issues. With this objective, we provide an overall picture of how viewability can fit within the ecosystem, which can help the different stakeholders to work on its adoption, integration and establishing a research agenda.This work was supported by the Plan de Doctorados Industriales de la Secretaría de Universidades e Investigación del Departamento de Empresa y Conocimiento de la Generalitat de Catalunya under the Grant 2018-DI-059 and by ExoClick. Furthermore, this work received support from the Fellowship through ‘‘la Caixa’’ Foundation under Grant ID100010434, from the European Union’s Horizon 2020 Research and Innovation Program through Marie Skłodowska-Curie Grant under Agreement 847648, from the Fellowship under Grant CF/BQ/PR20/11770009, from the Spanish Ministry of Economy and Competitiveness through Juan de la Cierva Formación Program under Grant FJCI-2017-34926 and from the Spanish Government under Grant PID2020-113795RB-C31 ‘‘COMPROMISE’’.Peer ReviewedPostprint (published version

(M)ad to see me?: Intelligent Advertisement Placement: Balancing User Annoyance and Advertising Effectiveness

Advertising is an unavoidable albeit a frustrating part of mobile interactions. Due to limited form factor, mobile advertisements often resort to intrusive strategies where they temporarily block the user's view in an attempt to increase effectiveness and force the user's attention. While such strategies contribute to advertising awareness and effectiveness, they do so at the cost of degrading the user's overall experience and can lead to frustration and annoyance. In this paper, we contribute by developing Perceptive Ads as an intelligent advertisement placement strategy that minimizes disruptions caused by ads while preserving their effectiveness. Our work is the first to simultaneously consider the needs of users, app developers, and advertisers. Ensuring the needs of all stakeholders are taken into account is essential for the adoption of advertising strategies as users (and indirectly developers) would reject strategies that are disruptive but effective, while advertisers would reject strategies that are non-disruptive but inefficient. We demonstrate the effectiveness of our technique through a user study with N = 16 participants and two representative examples of mobile apps that commonly integrate advertisements (a game and a news app). Results from the study demonstrate that our approach can improve perception towards advertisements by 43.75% without affecting application interactivity while at the same time increasing advertisement effectiveness by 37.5% compared to a state-of-the-art baseline.Peer reviewe

SmartChoices: Augmenting Software with Learned Implementations

We are living in a golden age of machine learning. Powerful models are being trained to perform many tasks far better than is possible using traditional software engineering approaches alone. However, developing and deploying those models in existing software systems remains difficult. In this paper we present SmartChoices, a novel approach to incorporating machine learning into mature software stacks easily, safely, and effectively. We explain the overall design philosophy and present case studies using SmartChoices within large scale industrial systems

FLINT: A Platform for Federated Learning Integration

Cross-device federated learning (FL) has been well-studied from algorithmic, system scalability, and training speed perspectives. Nonetheless, moving from centralized training to cross-device FL for millions or billions of devices presents many risks, including performance loss, developer inertia, poor user experience, and unexpected application failures. In addition, the corresponding infrastructure, development costs, and return on investment are difficult to estimate. In this paper, we present a device-cloud collaborative FL platform that integrates with an existing machine learning platform, providing tools to measure real-world constraints, assess infrastructure capabilities, evaluate model training performance, and estimate system resource requirements to responsibly bring FL into production. We also present a decision workflow that leverages the FL-integrated platform to comprehensively evaluate the trade-offs of cross-device FL and share our empirical evaluations of business-critical machine learning applications that impact hundreds of millions of users.Comment: Preprint for MLSys 202

Assisting Developers and Users in Developing and Choosing Efficient Mobile Device Apps

Les applications pour appareils mobiles jouent, de nos jours, un rôle important dans nos vies. Même si la consommation énergétique affecte la durée de vie de la batterie des appareils mobiles et limite l’utilisation des appareils, nous les utilisons presque partout, tout le temps et pour presque tout. Avec la croissance exponentielle du marché des applications pour appareils mobiles, les développeurs ont été témoins d’un changement radical dans le paysage du développement du logiciel. Les applications mobiles présentent de nouveaux défis dans la conception et l’implantation logicielle dus aux contraintes des ressources internes (tel que la batterie, le CPU et la mémoire) et externes (l’utilisation de donnés). Donc, les exigences traditionnelles non-fonctionnelles, tels que la fonctionnalité et la maintenabilité, ont été éclipsées par la performance. Les chercheurs étudient activement le rôle des pratiques de codage sur la consommation énergétique. Cependant, le CPU, la mémoire et les utilisations du réseau sont aussi des mesures importantes pour la performance. Même si le matériel informatique des appareils mobiles s’est beaucoup amélioré dans les dernières années, des nouveaux utilisateurs arrivent, possèdant des appareils bas de gamme avec accès limité aux données. Les développeurs doivent donc gérer les ressources attentivement car les nouveaux marchés possèdent une part importante des nouveaux utilisateurs qui se connectent en ligne pour la première fois. La performance des applications pour les appareils mobiles est donc un sujet très important. Des études récentes suggèrent que les ingénieurs logiciels peuvent aider à réduire la consommation énergétique en tenant compte des impacts de leurs décisions de conception et d’implantation sur l’énergie. Mais les décisions des développeurs ont un impact aussi sur le CPU, la mémoire et l’usage du réseau. Les développeurs doivent aussi prendre en considération la performance au moment d’évoluer le design de l’application des appareils mobiles. Le problème est que les développeurs n’ont pas de soutien pour comprendre l’impact de leurs décisions sur la performance de leurs apps. Ce problème est aussi vrai pour les utilisateurs d’appareils mobiles qui installent des apps en ignorant s’il existe des alternatives plus efficaces. Dans cette dissertation, nous aidons les développeurs et les utilisateurs à connaitre d’avantage l’impact de leurs décisions sur la performance des applications qu’ils développent et qu’ils consomment. Nous voulons aider les développeurs et les utilisateurs à développer et choisir des applications performantes. Nous fournissons des observations, des techniques et des lignes directrices qui aiderons les développeurs à prendre des décisions informées pour améliorer la performance de leurs applications. Nous proposons aussi une approche qui peut servir de complément aux marchés des applications pour appareils mobiles pour qu’ils puissent aider les développeurs et les utilisateurs à chercher des applications efficientes. Notre contribution est un pas précieux vers l’ingénierie de logiciels performants pour les applications des appareils mobiles et un avantage pour les utilisateurs d’appareils mobiles qui veulent utiliser des applications performantes.----------ABSTRACT: Mobile device applications (apps) play nowadays a central role in our life. Although energy consumption affects battery life of mobile devices and limits device use, we use them almost anywhere, all the time, and for almost everything. With the exponential growth of the market of mobile device apps in recent years, developers have witnessed a radical change in the landscape of software development. Mobile apps introduce new challenges in software design and implementation due to the constraints of internal resources (such as battery, CPU, and memory), as well as external resources (as data usage). Thus, traditional non-functional requirements, such as functionality and maintainability, have been overshadowed by performance. Researchers are actively investigating the role of coding practices on energy consumption. However, CPU, memory, and network usages are also important performance metrics. Although the hardware of mobile devices has considerably improved in recent years, emerging market users own low-devices and have limited access to data connection. Therefore, developers should manage resources mindfully because emerging markets own a significant share of the new users coming on-line for the first time. Thus, the performance of mobile device apps is a very important topic. Recent studies suggest that software engineers can help reduce energy consumption by considering the energy impacts of their design and implementation decisions. But developers’decisions also have an impact on CPU, memory, and network usages. So that, developers must take into account performance when evolving the design of mobile device apps. The problem is that mobile device app developers have no support to understand the impact of their decisions on their apps performance. This problem is also true for mobile device users who install apps ignoring if there exist more efficient alternatives. In this dissertation we help developers and users to know more about the impact of their decisions on the performance of apps they develop and consume, respectively. Thus, we want to assist developers and users in developing and choosing, respectively, efficient mobile device apps. We provide observations, techniques, and guidelines to help developers make informed decisions to improve the performance of their apps. We also propose an approach to complement mobile device app marketplaces to assist developers and users to search for efficient apps. Our contribution is a valuable step towards efficient software engineering for mobile device apps and a benefit for mobile device users who want to use efficient apps

Evolving SDN for Low-Power IoT Networks

Software Defined Networking (SDN) offers a flexible and scalable architecture that abstracts decision making away from individual devices and provides a programmable network platform. However, implementing a centralized SDN architecture within the constraints of a low-power wireless network faces considerable challenges. Not only is controller traffic subject to jitter due to unreliable links and network contention, but the overhead generated by SDN can severely affect the performance of other traffic. This paper addresses the challenge of bringing high-overhead SDN architecture to IEEE 802.15.4 networks. We explore how traditional SDN needs to evolve in order to overcome the constraints of low-power wireless networks, and discuss protocol and architectural optimizations necessary to reduce SDN control overhead - the main barrier to successful implementation. We argue that interoperability with the existing protocol stack is necessary to provide a platform for controller discovery and coexistence with legacy networks. We consequently introduce {\mu}SDN, a lightweight SDN framework for Contiki, with both IPv6 and underlying routing protocol interoperability, as well as optimizing a number of elements within the SDN architecture to reduce control overhead to practical levels. We evaluate {\mu}SDN in terms of latency, energy, and packet delivery. Through this evaluation we show how the cost of SDN control overhead (both bootstrapping and management) can be reduced to a point where comparable performance and scalability is achieved against an IEEE 802.15.4-2012 RPL-based network. Additionally, we demonstrate {\mu}SDN through simulation: providing a use-case where the SDN configurability can be used to provide Quality of Service (QoS) for critical network flows experiencing interference, and we achieve considerable reductions in delay and jitter in comparison to a scenario without SDN

A Utility-Preserving Obfuscation Approach for YouTube Recommendations

Online content platforms optimize engagement by providing personalized recommendations to their users. These recommendation systems track and profile users to predict relevant content a user is likely interested in. While the personalized recommendations provide utility to users, the tracking and profiling that enables them poses a privacy issue because the platform might infer potentially sensitive user interests. There is increasing interest in building privacy-enhancing obfuscation approaches that do not rely on cooperation from online content platforms. However, existing obfuscation approaches primarily focus on enhancing privacy but at the same time they degrade the utility because obfuscation introduces unrelated recommendations. We design and implement De-Harpo, an obfuscation approach for YouTube's recommendation system that not only obfuscates a user's video watch history to protect privacy but then also denoises the video recommendations by YouTube to preserve their utility. In contrast to prior obfuscation approaches, De-Harpo adds a denoiser that makes use of a "secret" input (i.e., a user's actual watch history) as well as information that is also available to the adversarial recommendation system (i.e., obfuscated watch history and corresponding "noisy" recommendations). Our large-scale evaluation of De-Harpo shows that it outperforms the state-of-the-art by a factor of 2x in terms of preserving utility for the same level of privacy, while maintaining stealthiness and robustness to de-obfuscation

CAMEO: A Middleware for Mobile Advertisement Delivery

National Research Foundation (NRF) Singapor