Do Memories Haunt You? An Automated Black Box Testing Approach for Detecting Memory Leaks in Android Apps

Memory leaks represent a remarkable problem for mobile app developers since a waste of memory due to bad programming practices may reduce the available memory of the device, slow down the apps, reduce their responsiveness and, in the worst cases, they may cause the crash of the app. A common cause of memory leaks in the specific context of Android apps is the bad handling of the events tied to the Activity Lifecycle. In order to detect and characterize these memory leaks, we present FunesDroid, a tool-supported black box technique for the automatic detection of memory leaks tied to the Activity Lifecycle in Android apps. FunesDroid implements a testing approach that can find memory leaks by analyzing unnecessary heap object replications after the execution of three different sequences of Activity Lifecycle events. In the paper, we present an exploratory study that shows the capability of the proposed technique to detect memory leaks and to characterize them in terms of their size, persistence and growth trend. The study also illustrates how memory leak causes can be detected with the support of the information provided by the FunesDroid tool

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

Healthcare rule-based expert system framework to help, educate and prepare users

Existe neste momento um grande problema com a abundância de informação relativa a saúde, a desinformação. Este problema tem causado que uma grande percentagem de pessoas faça visitas ao hospital desnecessariamente, ou pior. Como não foi possível encontrar uma aplicação móvel que disponibiliza um conjunto de informação cientificamente correta, disponível para todos, organizada e acessível, foi procurado neste documento resolver este problema. Para isto, um estudo foi conduzido para entender o estado da arte sobre aplicações móveis e sobre o ecossistema de aplicações para a saúde, desta forma endereçando diversos tópicos como acessibilidade, UX/UI e usabilidade. Para combater a desinformação, é importante ser capaz de disponibilizar aos utilizadores informação de confiança e para isso concluiu-se que a melhor e mais confiável informação teria que ser recolhida de especialistas os profissionais de saúde. Um protótipo foi construído que incorpora não só um sistema pericial que, baseado em regras criadas através da informação recolhida dos especialistas, podes disponibilizar um sistema de pesquisa de sintomas, mas também diversas funcionalidades de usabilidade e opções de acessibilidade. Este protótipo abre a possibilidade de desenvolver conjunto de novas funcionalidades e/ou melhorar aquelas já implementadas, como um algoritmo baseado em regras mais dinâmico ou testes de utilizadores mais extensos que permitem uma aplicação móvel mais completa em termos de acessibilidade e usabilidade.There is currently a major problem with the abundance of information regarding health, misinformation. This problem causes a large percentage of people that take unnecessary trips to the hospital or worse. As there is not a suitable mobile app that conveys a set of scientifically correct, easily available, and organized information that is accessible to everyone, in this document we sought to solve this issue. For this, a study was done to understand the state of the art of mobile applications and about the current ecosystem of healthcare apps, addressing several issues such as accessibility, UX/UI and usability. To tackle misinformation, it was important being able to provide reliable information to users, and therefore it was concluded that the best and most reliable information had to be gathered from experts, health professionals. A prototype was built that not only encompasses an expert-system that can, based on rules made with the information gathered from experts, provide a symptom search system, but also incorporates several usability features and accessibility options. This prototype opens the opportunity to develop a range of new features and/or enhance those already implemented, such as an improved and more dynamic rule-based algorithm or extensive user testing that allows for a full- fledged accessible and easy to use mobile application

Systems and Methods for Measuring and Improving End-User Application Performance on Mobile Devices

In today's rapidly growing smartphone society, the time users are spending on their smartphones is continuing to grow and mobile applications are becoming the primary medium for providing services and content to users. With such fast paced growth in smart-phone usage, cellular carriers and internet service providers continuously upgrade their infrastructure to the latest technologies and expand their capacities to improve the performance and reliability of their network and to satisfy exploding user demand for mobile data. On the other side of the spectrum, content providers and e-commerce companies adopt the latest protocols and techniques to provide smooth and feature-rich user experiences on their applications. To ensure a good quality of experience, monitoring how applications perform on users' devices is necessary. Often, network and content providers lack such visibility into the end-user application performance. In this dissertation, we demonstrate that having visibility into the end-user perceived performance, through system design for efficient and coordinated active and passive measurements of end-user application and network performance, is crucial for detecting, diagnosing, and addressing performance problems on mobile devices. My dissertation consists of three projects to support this statement. First, to provide such continuous monitoring on smartphones with constrained resources that operate in such a highly dynamic mobile environment, we devise efficient, adaptive, and coordinated systems, as a platform, for active and passive measurements of end-user performance. Second, using this platform and other passive data collection techniques, we conduct an in-depth user trial of mobile multipath to understand how Multipath TCP (MPTCP) performs in practice. Our measurement study reveals several limitations of MPTCP. Based on the insights gained from our measurement study, we propose two different schemes to address the identified limitations of MPTCP. Last, we show how to provide visibility into the end- user application performance for internet providers and in particular home WiFi routers by passively monitoring users' traffic and utilizing per-app models mapping various network quality of service (QoS) metrics to the application performance.PHDComputer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/146014/1/ashnik_1.pd

Performance Analysis of Family Welfare Empowerment Application: A Kanban Method Approach

This research examines the application of the Kanban method in testing a family welfare empowerment application. The Kanban method, initially developed by Toyota in manufacturing, has been effectively applied in software development. This study involves a series of tests involving various features within the application, such as user registration, village data collection, processing of the family welfare empowerment data at the Village/District level, and more. The test results show that most tests were successful, highlighting the application's success in executing essential functions such as user registration and event scheduling. However, some tests failed, primarily in inputting village, hamlet, and community unit data.  These results indicate that using the Kanban method in testing a family welfare empowerment application can potentially enhance development and testing efficiency. Metrics such as testing time, test success, and time efficiency have provided valuable insights into the application's performance. In conclusion, this testing provides a foundation for further application development, focusing on improving the areas that experienced testing failures. This research also opens up opportunities for further studies on using the Kanban method in software testing in various other application development contexts

"False negative -- that one is going to kill you": Understanding Industry Perspectives of Static Analysis based Security Testing

The demand for automated security analysis techniques, such as static analysis based security testing (SAST) tools continues to increase. To develop SASTs that are effectively leveraged by developers for finding vulnerabilities, researchers and tool designers must understand how developers perceive, select, and use SASTs, what they expect from the tools, whether they know of the limitations of the tools, and how they address those limitations. This paper describes a qualitative study that explores the assumptions, expectations, beliefs, and challenges experienced by developers who use SASTs. We perform in-depth, semi-structured interviews with 20 practitioners who possess a diverse range of software development expertise, as well as a variety of unique security, product, and organizational backgrounds. We identify $17$ key findings that shed light on developer perceptions and desires related to SASTs, and also expose gaps in the status quo - challenging long-held beliefs in SAST design priorities. Finally, we provide concrete future directions for researchers and practitioners rooted in an analysis of our findings.Comment: To be published in IEEE Symposium on Security and Privacy 202