Mobile applications play an important role in the dissemination of computing and information resources. They are often used in domains such as mobile banking, e-commerce, and health monitoring. Cost-effective testing techniques in these domains are critical. This dissertation contributes novel techniques for automatic construction of mobile application test suites. In particular, this work provides solutions that focus on the prohibitively large number of possible event sequences that must be sampled in GUI-based mobile applications. This work makes three major contributions: (1) an automated GUI testing tool, Autodroid, that implements a novel online approach to automatic construction of Android application test suites (2) probabilistic and combinatorial-based algorithms that systematically sample the input space of Android applications to generate test suites with GUI/context events and (3) empirical studies to evaluate the cost-effectiveness of our techniques on real-world Android applications. Our experiments show that our techniques achieve better code coverage and event coverage compared to random test generation. We demonstrate that our techniques are useful for automatic construction of Android application test suites in the absence of source code and preexisting abstract models of an Application Under Test (AUT). The insights derived from our empirical studies provide guidance to researchers and practitioners involved in the development of automated GUI testing tools for Android applications.

Mobile applications are growing in popularity and pose new problems in the area of software testing. In particular, mobile applications heavily depend upon user interactions and a dynamically changing environment of system events. In this thesis, we focus on user-driven events and use Q-learning, a reinforcement machine learning algorithm, to generate tests for Android applications under test (AUT). We implement a framework that automates the generation of GUI test cases by using our Q-learning approach and compare it to a uniform random (UR) implementation. A novel feature of our approach is that we generate user-driven event sequences through the GUI, without the source code or the model of the AUT. Hence, considerable amount of cost and time are saved by avoiding the need for model generation for generating the tests. Our results show that the systematic path exploration used by Q-learning results in higher average code coverage in comparison to the uniform random approach.

Após a redução dos estrógenos no período da menopausa algumas mulheres passam a perder massa óssea acima de 1% ao ano chegando ao final de cinco anos com perda superior a 25%. Neste sentido, fatores como idade avançada, baixa ingestão de cálcio e menopausa precoce favorecem o aparecimento da osteoporose. Métodos preventivos como orientação nutricional para uma dieta adequada e o apoio da tecnologia por meio de aplicativos que avaliam o consumo alimentar são essenciais. Desta forma, objetivou-se neste estudo desenvolver um aplicativo em plataforma Android® voltado à avaliação das condições nutricionais e orgânicas envolvidas na saúde óssea e grau de risco para o desenvolvimento de osteoporose em mulheres pós-menopáusicas. Para o alcance deste objetivo procedeu-se um estudo com 72 mulheres com idade entre 46 a 79 anos, provenientes do programa de exercícios físicos para a saúde óssea do Laboratório de Pesquisas em Bioquímica e Densitometria da Universidade Tecnológica Federal do Paraná. Os dados foram coletados no segundo semestre de 2014 por meio de exames de Densitometria Óssea e Composição Corporal, Exames de Sangue, dados Antropométricos e Avaliação Nutricional. Foram incluídas no estudo mulheres com diagnóstico atualizado de osteopenia ou osteoporose primária, com idade igual ou superior a 45 anos em fase pós-menopáusica. Para a avaliação da densidade mineral óssea e composição corporal utilizou-se o aparelho de Absortometria de Dupla Energia de Raios-X (DXA) da marca HologicTM modelo Discovery A. Para a avaliação antropométrica foi incluído a Massa Corporal, Estatura, Circunferência Abdominal, Circunferência da Cintura e Circunferência do Quadril. O instrumento para avaliação de consumo alimentar utilizado foi o Recordatório de 24 horas de um dia (R24h). A estimativa da ingestão de energia e nutrientes foi realizada a partir da tabulação dos alimentos ingeridos no Software Diet Pro 4®. Em uma sub amostra com 30 mulheres com osteopenia/osteoporose foram realizados os exames de cálcio sérico e fosfatase alcalina. Os resultados demonstraram no grupo de mulheres (n=30) ingestão média de cálcio de 570mg/dia (±340). A análise do cálcio sérico apresentou média dentro da normalidade (10,20mg/dl±0,32) e valores médios e fosfatase alcalina ligeiramente aumentados (105,40 U/L±23,70). Ainda, houve importante correlação entre o consumo de ideal de proteínas e o consumo de cálcio diário (0,375 p valor 0,05). Com base nestes achados, foi desenvolvido um aplicativo fase inicial na plataforma Android® do sistema operacional do Google®, sendo denominado de OsteoNutri. Optou-se pela utilização Java Eclipse® onde nele foram executados a versão Android® do projeto; escolha de ícones de aplicação e configuração do editor visual para construção dos layouts do aplicativo. Foi utilizado o DroidDraw® para desenvolvimento das três interfaces gráficas do aplicativo. Para os testes práticos utilizou-se um celular compatível com a versão que foi criada (4.4 ou superior). O protótipo foi desenvolvido em conjunto com o Grupo de Desenvolvimento Aplicativos e Instrumentação (GDAI) da Universidade Tecnológica Federal do Paraná. Portanto, este aplicativo pode ser considerado uma importante ferramenta no controle dietético, possibilitando controle mais próximo de consumo de Cálcio e Proteínas dietéticas. / Following a drop in estrogen in the period of menopause some women begin to lose bone mass more than 1% per year reaching the end of five years with loss greater than 25%. In this regard, factors such as older age, low calcium intake and premature menopause favor the onset of osteoporosis. Preventive methods such as nutritional counseling to a proper diet and the support of technology through applications that assess dietary intake are essential. Thus, this study aimed to develop an application for Android® platform focused on the evaluation of nutritional and organic conditions involved in bone health and risks for developing osteoporosis in postmenopausal women. To achieve this goal we proceeded to a study of 72 women aged 46-79 years, from the physical exercise for bone health of the Laboratory for Research in Biochemistry and Densitometry the Federal Technological University of Paraná program. Data were collected in the second half of 2014 through tests Bone Densitometry and Body Composition, Blood Tests, Anthropometric data and Nutrition Assessment. The study included women with a current diagnosis of osteopenia or osteoporosis primary, aged more than 45 years postmenopausal. For the assessment of bone mineral density and body composition used the device Absorptiometry Dual Energy X-ray (DXA) brand Hologic Discovery TM Model A. For anthropometric assessment was included to body mass, height, abdominal circumference, Waist circumference and hip circumference. The instrument for assessing food consumption was used Recall 24 hours a day (24HR). The estimated intake of energy and nutrients was carried from the tabulation of the food eaten in the Software Diet Pro 4®. In a sub sample of 30 women with osteopenia / osteoporosis serum calcium and alkaline phosphatase tests were performed. The results demonstrated a group of women (n = 30) average calcium intake of 570mg / day (± 340). The analysis showed a mean serum calcium within the normal range (10,20mg / dl ± 0.32) and average values and slightly increased alkaline phosphatase (105.40 U / L ± 23.70). Furthermore, there was a significant correlation between the consumption of protein and the optimal daily intake of calcium (0.375 p-value 0.05). Based on these findings, we developed an application early stage in Android® platform operating system Google®, being called OsteoNutri. We chose to use Java Eclipse® where it was executed Android® version of the project; choice of application icons and setting the visual editor for building the application layouts. The DroidDraw® was used for development of the three application GUIs. For practical tests we used a cell compatible with the version that was created (4.4 or higher). The prototype was developed in conjunction with the Group and Instrumentation Applications Development (GDAI) of the Federal Technological University of Paraná. So this application can be considered an important tool in dietary control, allowing closer control consumption of calcium and dietary proteins.