A Study of Non-Functional Requirements in Apps for Mobile Devices

Nowadays, no one questions the crucial role of Requirements Engi-neering in software systems development. Specifically, if apps are generated for execution on mobile devices, certain non-functional requirements become high-ly relevant. In this article, an experimental study on three non-functional re-quirements that are essential for the development of native and multi-platform mobile apps is detailed. These requirements are performance, energy consump-tion and storage space utilization.Instituto de Investigación en Informátic

A Study of Non-Functional Requirements in Apps for Mobile Devices

Nowadays, no one questions the crucial role of Requirements Engi-neering in software systems development. Specifically, if apps are generated for execution on mobile devices, certain non-functional requirements become high-ly relevant. In this article, an experimental study on three non-functional re-quirements that are essential for the development of native and multi-platform mobile apps is detailed. These requirements are performance, energy consump-tion and storage space utilization.Instituto de Investigación en Informátic

Enhancing Mobile Device System using Information from Users and Upper Layers

Despite the rapid hardware upgrades, a common complaint among smartphone owners is the poor battery life. to many users, being required to charge the smartphone after a single day of moderate usage is unacceptable. Moreover, current smartphones suffer various unpredictable delays during operation, e.g., when launching an app, leading to poor user experience. In this dissertation, we provide solutions that enhance systems on portable devices using information obtained from their users and upper layers on the I/O path. First, we provide an experimental study on how storage I/O path upper layers affect power levels in smartphones, and introduce energy-efficient approaches to reduce energy consumption facilitating various usage patterns. at each layer, we investigate the amount of energy that can be saved, and use that to design and implement a prototype with optimal energy savings named SmartStorage. We evaluate our prototype by using the 20 most popular android applications, and our energy-efficient approaches achieve from 23% to 52% of energy savings compared to using the current techniques. Next, we conduct the first large-scale user study on the I/O delay of android using the data collected from our android app running on 2611 devices within nine months. Among other factors, we observe that reads experience up to 626% slowdown when blocked by concurrent writes for certain workloads. We use this obtained knowledge to design a system called SmartIO that reduces application delays by prioritizing reads over writes. SmartIO is evaluated extensively on several groups of popular applications. The results show that our system reduces launch delays by up to 37.8%, and run-time delays by up to 29.6%. Finally, we study the impact of memory on smartphone user-perceived performance. Our heap usage investigation of 20 popular applications indicates that rich multimedia applications have high heap usage and go above allowed boundaries, up to 5.63 times more heap than guaranteed by the system, and may cause crashes and erroneous behaviors. Moreover, limited heap may not only cause an app to crash, but may even prevent an app from launching. Therefore, we present iRAM, a system that maintains optimal heap size limits to avoid crashes, efficiently maximizes free memory levels, and cleans low-priority processes to reduce application delays. The evaluation indicates that iRAM reduces application crashes by up to 14 percent

A Survey of Performance Optimization for Mobile Applications

Nowadays there is a mobile application for almost everything a user may think of, ranging from paying bills and gathering information to playing games and watching movies. In order to ensure user satisfaction and success of applications, it is important to provide high performant applications. This is particularly important for resource constraint systems such as mobile devices. Thereby, non-functional performance characteristics, such as energy and memory consumption, play an important role for user satisfaction. This paper provides a comprehensive survey of non-functional performance optimization for Android applications. We collected 155 unique publications, published between 2008 and 2020, that focus on the optimization of non-functional performance of mobile applications. We target our search at four performance characteristics, in particular: responsiveness, launch time, memory and energy consumption. For each performance characteristic, we categorize optimization approaches based on the method used in the corresponding publications. Furthermore, we identify research gaps in the literature for future work

Improving the Efficacy of Context-Aware Applications

In this dissertation, we explore methods for enhancing the context-awareness capabilities of modern computers, including mobile devices, tablets, wearables, and traditional computers. Advancements include proposed methods for fusing information from multiple logical sensors, localizing nearby objects using depth sensors, and building models to better understand the content of 2D images. First, we propose a system called Unagi, designed to incorporate multiple logical sensors into a single framework that allows context-aware application developers to easily test new ideas and create novel experiences. Unagi is responsible for collecting data, extracting features, and building personalized models for each individual user. We demonstrate the utility of the system with two applications: adaptive notification filtering and a network content prefetcher. We also thoroughly evaluate the system with respect to predictive accuracy, temporal delay, and power consumption. Next, we discuss a set of techniques that can be used to accurately determine the location of objects near a user in 3D space using a mobile device equipped with both depth and inertial sensors. Using a novel chaining approach, we are able to locate objects farther away than the standard range of the depth sensor without compromising localization accuracy. Empirical testing shows our method is capable of localizing objects 30m from the user with an error of less than 10cm. Finally, we demonstrate a set of techniques that allow a multi-layer perceptron (MLP) to learn resolution-invariant representations of 2D images, including the proposal of an MCMC-based technique to improve the selection of pixels for mini-batches used for training. We also show that a deep convolutional encoder could be trained to output a resolution-independent representation in constant time, and we discuss several potential applications of this research, including image resampling, image compression, and security

Mobile Sensing Systems

[EN] Rich-sensor smart phones have made possible the recent birth of the mobile sensing research area as part of ubiquitous sensing which integrates other areas such as wireless sensor networks and web sensing. There are several types of mobile sensing: individual, participatory, opportunistic, crowd, social, etc. The object of sensing can be people-centered or environment-centered. The sensing domain can be home, urban, vehicular Currently there are barriers that limit the social acceptance of mobile sensing systems. Examples of social barriers are privacy concerns, restrictive laws in some countries and the absence of economic incentives that might encourage people to participate in a sensing campaign. Several technical barriers are phone energy savings and the variety of sensors and software for their management. Some existing surveys partially tackle the topic of mobile sensing systems. Published papers theoretically or partially solve the above barriers. We complete the above surveys with new works, review the barriers of mobile sensing systems and propose some ideas for efficiently implementing sensing, fusion, learning, security, privacy and energy saving for any type of mobile sensing system, and propose several realistic research challenges. The main objective is to reduce the learning curve in mobile sensing systems where the complexity is very high.This work has been partially supported by the "Ministerio de Ciencia e Innovacion", through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental", project TEC2011-27516, and by the Polytechnic University of Valencia, through the PAID-05-12 multidisciplinary projects.Macias Lopez, EM.; Suarez Sarmiento, A.; Lloret, J. (2013). Mobile Sensing Systems. Sensors. 13(12):17292-17321. https://doi.org/10.3390/s131217292S1729217321131

Enfoques de desarrollo de Aplicaciones Móviles: un análisis de parámetros para la toma de decisiones

La masificación de los dispositivos móviles con gran capacidad de cómputo ha traído gran relevancia al desarrollo de aplicaciones móviles. Este tipo de desarrollo tiene características propias que no estaban presentes en el desarrollo tradicional de software. Deben tenerse en cuenta requisitos como los tiempos del mercado, las limitaciones de los dispositivos móviles, la diversidad de plataformas, entre muchos otros. Para maximizar la presencia en el mercado, puede optarse por desarrollar aplicaciones específicas para cada plataforma, con varios desarrollos en paralelo usando herramientas y lenguajes propios de cada una, lo que se denomina enfoque nativo. Otra opción es realizar un desarrollo único, usando herramientas que permitan llevar la aplicación a más de una plataforma, lo cual se denomina enfoque multiplataforma. La presente tesina consiste en la investigación de los diferentes enfoques de desarrollo de aplicaciones móviles, y el desarrollo de experimentos para conformar un marco comparativo entre los enfoques. Se implementaron aplicaciones en todos los enfoques estudiados, y compararon según parámetros que pueden influir en el éxito de una aplicación, tales como el uso de energía de la batería o el espacio de almacenamiento ocupado.Facultad de Informátic