Analysis of the Impact of Performance on Apps Retention

The non-stopping expansion of mobile technologies has produced the swift increase of smartphones with higher computational power, and sophisticated sensing and communication capabilities have provided the foundations to develop apps on the move with PC-like functionality. Indeed, nowadays apps are almost everywhere, and their number has increased exponentially with Apple AppStore, Google Play and other mobile app marketplaces offering millions of apps to users. In this scenario, it is common to find several apps providing similar functionalities to users. However, only a fraction of these applications has a long-term survival rate in app stores. Retention is a metric widely used to quantify the lifespan of mobile apps. Higher app retention corresponds to higher adoption and level of engagement. While existing scientific studies have analysed mobile users' behaviour and support the existence of factors that influence apps retention, the quantification about how do these factors affect long-term usage is still missing. In this thesis, we contribute to these studies quantifying and modelling one of the critical factors that affect app retention: performance. We deepen the analysis of performance based on two key-related variables: network connectivity and battery consumption. The analysis is performed by combining two large-scale crowdsensed datasets. The first includes measurements about network quality and the second about app usage and energy consumption. Our results show the benefits of data fusion to introduce richer contexts impossible of being discovered when analysing data sources individually. We also demonstrate that, indeed, high variations of these variables together and individually affect the likelihood of long-term app usage. But also, that retention is regulated by what users consider reasonable standards of performance, meaning that the improvement of latency and energy consumption does not guarantee higher retention. To provide further insights, we develop a model to predict retention using performance-related variables. Its accuracy in the results allows generalising the effect of performance in long-term usage across categories, locations and moderating variables

Towards a Practical Pedestrian Distraction Detection Framework using Wearables

Pedestrian safety continues to be a significant concern in urban communities and pedestrian distraction is emerging as one of the main causes of grave and fatal accidents involving pedestrians. The advent of sophisticated mobile and wearable devices, equipped with high-precision on-board sensors capable of measuring fine-grained user movements and context, provides a tremendous opportunity for designing effective pedestrian safety systems and applications. Accurate and efficient recognition of pedestrian distractions in real-time given the memory, computation and communication limitations of these devices, however, remains the key technical challenge in the design of such systems. Earlier research efforts in pedestrian distraction detection using data available from mobile and wearable devices have primarily focused only on achieving high detection accuracy, resulting in designs that are either resource intensive and unsuitable for implementation on mainstream mobile devices, or computationally slow and not useful for real-time pedestrian safety applications, or require specialized hardware and less likely to be adopted by most users. In the quest for a pedestrian safety system that achieves a favorable balance between computational efficiency, detection accuracy, and energy consumption, this paper makes the following main contributions: (i) design of a novel complex activity recognition framework which employs motion data available from users' mobile and wearable devices and a lightweight frequency matching approach to accurately and efficiently recognize complex distraction related activities, and (ii) a comprehensive comparative evaluation of the proposed framework with well-known complex activity recognition techniques in the literature with the help of data collected from human subject pedestrians and prototype implementations on commercially-available mobile and wearable devices

Teenustele orienteeritud ja tõendite-teadlik mobiilne pilvearvutus

Arvutiteaduses on kaks kõige suuremat jõudu: mobiili- ja pilvearvutus. Kui pilvetehnoloogia pakub kasutajale keerukate ülesannete lahendamiseks salvestus- ning arvutusplatvormi, siis nutitelefon võimaldab lihtsamate ülesannete lahendamist mistahes asukohas ja mistahes ajal. Täpsemalt on mobiilseadmetel võimalik pilve võimalusi ära kasutades energiat säästa ning jagu saada kasvavast jõudluse ja ruumi vajadusest. Sellest tulenevalt on käesoleva töö peamiseks küsimuseks kuidas tuua pilveinfrastruktuur mobiilikasutajale lähemale? Antud töös uurisime kuidas mobiiltelefoni pilveteenust saab mobiilirakendustesse integreerida. Saime teada, et töö delegeerimine pilve eeldab mitmete pilve aspektide kaalumist ja integreerimist, nagu näiteks ressursimahukas töötlemine, asünkroonne suhtlus kliendiga, programmaatiline ressursside varustamine (Web APIs) ja pilvedevaheline kommunikatsioon. Nende puuduste ületamiseks lõime Mobiilse pilve vahevara Mobile Cloud Middleware (Mobile Cloud Middleware - MCM) raamistiku, mis kasutab deklaratiivset teenuste komponeerimist, et delegeerida töid mobiililt mitmetele pilvedele kasutades minimaalset andmeedastust. Teisest küljest on näidatud, et koodi teisaldamine on peamisi strateegiaid seadme energiatarbimise vähendamiseks ning jõudluse suurendamiseks. Sellegipoolest on koodi teisaldamisel miinuseid, mis takistavad selle laialdast kasutuselevõttu. Selles töös uurime lisaks, mis takistab koodi mahalaadimise kasutuselevõttu ja pakume lahendusena välja raamistiku EMCO, mis kogub seadmetelt infot koodi jooksutamise kohta erinevates kontekstides. Neid andmeid analüüsides teeb EMCO kindlaks, mis on sobivad tingimused koodi maha laadimiseks. Võrreldes kogutud andmeid, suudab EMCO järeldada, millal tuleks mahalaadimine teostada. EMCO modelleerib kogutud andmeid jaotuse määra järgi lokaalsete- ning pilvejuhtude korral. Neid jaotusi võrreldes tuletab EMCO täpsed atribuudid, mille korral mobiilirakendus peaks koodi maha laadima. Võrreldes EMCO-t teiste nüüdisaegsete mahalaadimisraamistikega, tõuseb EMCO efektiivsuse poolest esile. Lõpuks uurisime kuidas arvutuste maha laadimist ära kasutada, et täiustada kasutaja kogemust pideval mobiilirakenduse kasutamisel. Meie peamiseks motivatsiooniks, et sellist adaptiivset tööde täitmise kiirendamist pakkuda, on tagada kasutuskvaliteet (QoE), mis muutub vastavalt kasutajale, aidates seeläbi suurendada mobiilirakenduse eluiga.Mobile and cloud computing are two of the biggest forces in computer science. While the cloud provides to the user the ubiquitous computational and storage platform to process any complex tasks, the smartphone grants to the user the mobility features to process simple tasks, anytime and anywhere. Smartphones, driven by their need for processing power, storage space and energy saving are looking towards remote cloud infrastructure in order to solve these problems. As a result, the main research question of this work is how to bring the cloud infrastructure closer to the mobile user? In this thesis, we investigated how mobile cloud services can be integrated within the mobile apps. We found out that outsourcing a task to cloud requires to integrate and consider multiple aspects of the clouds, such as resource-intensive processing, asynchronous communication with the client, programmatically provisioning of resources (Web APIs) and cloud intercommunication. Hence, we proposed a Mobile Cloud Middleware (MCM) framework that uses declarative service composition to outsource tasks from the mobile to multiple clouds with minimal data transfer. On the other hand, it has been demonstrated that computational offloading is a key strategy to extend the battery life of the device and improves the performance of the mobile apps. We also investigated the issues that prevent the adoption of computational offloading, and proposed a framework, namely Evidence-aware Mobile Computational Offloading (EMCO), which uses a community of devices to capture all the possible context of code execution as evidence. By analyzing the evidence, EMCO aims to determine the suitable conditions to offload. EMCO models the evidence in terms of distributions rates for both local and remote cases. By comparing those distributions, EMCO infers the right properties to offload. EMCO shows to be more effective in comparison with other computational offloading frameworks explored in the state of the art. Finally, we investigated how computational offloading can be utilized to enhance the perception that the user has towards an app. Our main motivation behind accelerating the perception at multiple response time levels is to provide adaptive quality-of-experience (QoE), which can be used as mean of engagement strategy that increases the lifetime of a mobile app

An Empirical Investigation of Performance Overhead in Cross-Platform Mobile Development Frameworks

The heterogeneity of the leading mobile platforms in terms of user interfaces, user experience, programming language, and ecosystem have made cross-platform development frameworks popular. These aid the creation of mobile applications – apps – that can be executed across the target platforms (typically Android and iOS) with minimal to no platform-specific code. Due to the cost- and time-saving possibilities introduced through adopting such a framework, researchers and practitioners alike have taken an interest in the underlying technologies. Examining the body of knowledge, we, nonetheless, frequently encounter discussions on the drawbacks of these frameworks, especially with regard to the performance of the apps they generate. Motivated by the ongoing discourse and a lack of empirical evidence, we scrutinised the essential piece of the cross-platform frameworks: the bridge enabling cross-platform code to communicate with the underlying operating system and device hardware APIs. The study we present in the article benchmarks and measures the performance of this bridge to reveal its associated overhead in Android apps. The development of the artifacts for this experiment was conducted using five cross-platform development frameworks to generate Android apps, in addition to a baseline native Android app implementation. Our results indicate that – for Android apps – the use of cross-platform frameworks for the development of mobile apps may lead to decreased performance compared to the native development approach. Nevertheless, certain cross-platform frameworks can perform equally well or even better than native on certain metrics which highlights the importance of well-defined technical requirements and specifications for deliberate selection of a cross-platform framework or overall development approach.publishedVersio

A Behavioral Model System for Implicit Mobile Authentication

Smartphones are increasingly essential to users’ everyday lives. Security concerns of data compromises are growing, and explicit authentication methods are proving to be inconvenient and insufficient. Meanwhile, users demand quicker and more secure authentication. To address this, a user can be authenticated continuously and implicitly, through understanding consistency in their behavior. This research project develops a Behavioral Model System (BMS) that records users’ behavioral metrics on an Android device and sends them to a server to develop a behavioral model for the user. Once a strong model is generated with TensorFlow, a user’s most recent behavior is queried against the model to authenticate them. The model is tested across its metrics to evaluate the reliability of BMS

Google Play apps ERM: (energy rating model) multi-criteria evaluation model to generate tentative energy ratings for Google Play store apps

A common issue that is shared among Android smartphones users was and still related to saving their batteries power and to avoid the need of using any recharging resources. The tremendous increase in smartphone usage is clearly accompanied by an increase in the need for more energy. This preoperational relationship between modern technology and energy generates energy-greedy apps, and therefore power-hungry end users. With many apps falling under the same category in an app store, these apps usually share similar functionality. Because developers follow different design and development schools, each app has its own energy-consumption habits. Since apps share similar features, an end-user with limited access to recharging resources would prefer an energy-friendly app rather than a popular energy-greedy app. However, app stores give no indication about the energy behaviour of the apps they offer, which causes users to randomly choose apps without understanding their energy-consumption behaviour. Furthermore, with regard to the research questions about the fact that power saving application consumes a lot of electricity, past studies clearly indicate that there is a lot of battery depletion due to several factors. This problem has become a major concern for smartphone users and manufacturers. The main contribution of our research is to design a tool that can act as an effective decision support factor for end users to have an initial indication of the energy-consumption behaviour of an application before installing it. The core idea of the “before-installation” philosophy is simplified by the contradicting concept of installing the app and then having it monitored and optimized. Since processing requires power, avoiding the consumption of some power in order to conserve a larger amount of power should be our priority. So instead, we propose a preventive strategy that requires no processing on any layer of the smartphone. To address this issue, we propose a star-rating evaluation model (SREM), an approach that generates a tentative energy rating label for each app. To that end, SREM adapts current energy-aware refactoring tools to demonstrate the level of energy consumption of an app and presents it in a star-rating schema similar to the Ecolabels used on electrical home appliances. The SREM will also inspire developers and app providers to come up with multiple energy-greedy versions of the same app in order to suit the needs of different categories of users and rate their own apps. We proposed adding SREM to Google Play store in order to generate the energy-efficiency label for each app which will act as a guide for both end users and developers without running any processes on the end-users smartphone. Our research also reviews relevant existing literature specifically those covering various energy-saving techniques and tools proposed by various authors for Android smartphones. A secondary analysis has been done by evaluating the past research papers and surveys that has been done to assess the perception of the users regarding the phone power from their battery. In addition, the research highlights an issue that the notifications regarding the power saving shown on the screen seems to exploit a lot of battery. Therefore, this study has been done to reflect the ways that could help the users to save the phone battery without using any power from the same battery in an efficient manner. The research offers an insight into new ways that could be used to more effectively conserve smartphone energy, proposing a framework that involves end users on the process.Um problema comum entre utilizadores de smartphones Android tem sido a necessidade de economizar a energia das baterias, de modo a evitar a utilização de recursos de recarga. O aumento significativo no uso de smartphones tem sido acompanhado por um aumento, também significativo, na necessidade de mais energia. Esta relação operacional entre tecnologia moderna e energia gera aplicações muito exigentes no seu consumo de energia e, portanto, perfis de utilizadores que requerem níveis de energia crescentes. Com muitos das aplicações que se enquadram numa mesma categoria da loja de aplicações (Google Store), essas aplicações geralmente também partilham funcionalidades semelhantes. Como os criadores destas aplicações seguem abordagens diferentes de diversas escolas de design e desenvolvimento, cada aplicação possui as suas próprias caraterísticas de consumo de energia. Como as aplicações partilham recursos semelhantes, um utilizador final com acesso limitado a recursos de recarga prefere uma aplicação que consome menos energia do que uma aplicação mais exigente em termos de consumo energético, ainda que seja popular. No entanto, as lojas de aplicações não fornecem uma indicação sobre o comportamento energético das aplicações oferecidas, o que faz com que os utilizadores escolham aleatoriamente as suas aplicações sem entenderem o correspondente comportamento de consumo de energia. Adicionalmente, no que diz respeito à questão de investigação, a solução de uma aplicação de economia de energia consume muita eletricidade, o que a torna limitada; estudos anteriores indicam claramente que há muita perda de bateria devido a vários fatores, não constituindo solução para muitos utilizadores e para os fabricantes de smartphones. A principal contribuição de nossa pesquisa é projetar uma ferramenta que possa atuar como um fator de suporte à decisão eficaz para que os utilizadores finais tenham uma indicação inicial do comportamento de consumo de energia de uma aplicação, antes de a instalar. A ideia central da filosofia proposta é a de atuar "antes da instalação", evitando assim a situação em se instala uma aplicação para perceber à posteriori o seu impacto no consumo energético e depois ter que o monitorizar e otimizar (talvez ainda recorrendo a uma aplicação de monitorização do consumo da bateria, o que agrava ainda mais o consumo energético). Assim, como o processamento requer energia, é nossa prioridade evitar o consumo de alguma energia para conservar uma quantidade maior de energia. Portanto, é proposta uma estratégia preventiva que não requer processamento em nenhuma camada do smartphone. Para resolver este problema, é proposto um modelo de avaliação por classificação baseado em níveis e identificado por estrelas (SREM). Esta abordagem gera uma etiqueta de classificação energética provisória para cada aplicação. Para isso, o SREM adapta as atuais ferramentas de refatoração com reconhecimento de energia para demonstrar o nível de consumo de energia de uma aplicação, apresentando o resultado num esquema de classificação por estrelas semelhante ao dos rótulos ecológicos usados em eletrodomésticos. O SREM também se propõe influenciar quem desenvolve e produz as aplicações, a criarem diferentes versões destas, com diferentes perfis de consumo energético, de modo a atender às necessidades de diferentes categorias de utilizadores e assim classificar as suas próprias aplicações. Para avaliar a eficiência do modelo como um complemento às aplicações da loja Google Play, que atuam como uma rotulagem para orientação dos utilizadores finais. A investigação também analisa a literatura existente relevante, especificamente a que abrange as várias técnicas e ferramentas de economia de energia, propostas para smartphones Android. Uma análise secundária foi ainda realizada, focando nos trabalhos de pesquisa que avaliam a perceção dos utilizadores em relação à energia do dispositivo, a partir da bateria. Em complemento, a pesquisa destaca um problema de que as notificações sobre a economia de energia mostradas na tela parecem explorar muita bateria. Este estudo permitiu refletir sobre as formas que podem auxiliar os utilizadores a economizar a bateria do telefone sem usar energia da mesma bateria e, mesmo assim, o poderem fazer de maneira eficiente. A pesquisa oferece uma visão global das alternativas que podem ser usadas para conservar com mais eficiência a energia do smartphone, propondo um modelo que envolve os utilizadores finais no processo.Un problème fréquent rencontré par les utilisateurs de smartphones Android a été, tout en l’étant toujours, d’économiser leur batterie et d’éviter la nécessité d’utiliser des ressources de recharge. La croissance considérable de l’utilisation des smartphones s’accompagne clairement d’une augmentation des besoins en énergie. Cette relation préopérationnelle entre la technologie moderne et l’énergie génère des applications gourmandes en énergie, et donc des utilisateurs finaux qui le sont tout autant. De nombreuses applications relevant de la même catégorie dans une boutique partagent généralement des fonctionnalités similaires. Étant donné que les développeurs adoptent différentes approches de conception et de développement, chaque application a ses propres caractéristiques de consommation d’énergie. Comme les applications partagent des fonctionnalités similaires, un utilisateur final disposant d’un accès limité aux ressources de recharge préférerait une application écoénergétique plutôt qu’une autre gourmande en énergie. Cependant, les boutiques d’applications ne donnent aucune indication sur le comportement énergétique des applications qu’elles proposent, ce qui incite les utilisateurs à choisir des applications au hasard sans comprendre leurs caractéristiques en ce domaine. En outre, en ce qui concerne les questions de recherche sur le fait que les applications d’économie d’énergie consomment beaucoup d’électricité, des études antérieures indiquent clairement que la décharge d’une batterie est due à plusieurs facteurs. Ce problème est devenu une préoccupation majeure pour les utilisateurs et les fabricants de smartphones. La principale contribution de notre étude est de concevoir un outil qui peut agir comme un facteur d’aide efficace à la décision pour que les utilisateurs finaux aient une indication initiale du comportement de consommation d’énergie d’une application avant de l’installer. L’idée de base de la philosophie « avant l’installation » est simplifiée par le concept contradictoire d’installer l’application pour ensuite la contrôler et l’optimiser. Puisque les opérations de traitement exigent de l’énergie, éviter la consommation d’une partie d’entre elles pour l’économiser devrait être notre priorité. Nous proposons donc une stratégie préventive qui ne nécessite aucun traitement sur une couche quelconque du smartphone. Pour résoudre ce problème, nous proposons un modèle d’évaluation au moyen d’étoiles (star-rating evaluation model ou SREM), une approche qui génère une note énergétique indicative pour chaque application. À cette fin, le SREM adapte les outils actuels de refactoring sensibles à l’énergie pour démontrer le niveau de consommation d’énergie d’une application et la présente dans un schéma de classement par étoiles similaire aux labels écologiques utilisés sur les appareils électroménagers. Le SREM incitera également les développeurs et les fournisseurs d’applications à mettre au point plusieurs versions avides d’énergie d’une même application afin de répondre aux besoins des différentes catégories d’utilisateurs et d’évaluer leurs propres applications. Nous avons proposé d’ajouter le SREM au Google Play Store afin de générer le label d’efficacité énergétique pour chaque application. Celui-ci servira de guide à la fois pour les utilisateurs finaux et les développeurs sans exécuter de processus sur le smartphone des utilisateurs finaux. Notre recherche passe également en revue la littérature existante pertinente, en particulier celle qui couvre divers outils et techniques d’économie d’énergie proposés par divers auteurs pour les smartphones Android. Une analyse secondaire a été effectuée en évaluant les documents de recherche et les enquêtes antérieurs qui ont été réalisés pour évaluer la perception des utilisateurs concernant l’alimentation téléphonique depuis leur batterie. En outre, l’étude met en évidence un problème selon lequel les notifications concernant les économies d’énergie affichées à l’écran semblent elles-mêmes soumettre les batteries à une forte utilisation. Par conséquent, cette étude a été entreprise pour refléter les façons qui pourraient aider les utilisateurs à économiser efficacement la batterie de leur téléphone sans pour autant la décharger. L’étude offre un bon aperçu des nouvelles façons d’économiser plus efficacement l’énergie des smartphones, en proposant un cadre qui implique les utilisateurs finaux dans le processus

The Evolution of Android Malware and Android Analysis Techniques

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