Desenvolvimento do aplicativo educacional meu texto para plataforma android

TCC (graduação) - Universidade Federal de Santa Catarina. Centro de Ciências, Tecnologias e Saúde (CTS). Tecnologia da Informação e Comunicação.A educação vem mudando em passos muito lentos desde os primórdios, mas quando se trata das duas últimas décadas, vimos um grande avanço tecnológico e com ele uma preocupação com a maneira de ensino datada que ainda se tem. Com a popularização da internet e a disseminação de informação de forma rápida e ao alcance de uma maioria, cria-se uma modalidade de ensino usando a tecnologia e internet como bens primordiais para uma educação de grande qualidade e de forma completa, assim como a possibilidade de compartilhamento mundial. Este projeto é composto como uma continuação de trabalhos anteriores do curso de Tecnologia da Informação e Comunicação da Universidade Federal de Santa Catarina (UFSC), em parceria com um projeto de mestrado realizado no Instituto Federal de Santa Catarina (IFSC), no intuito de melhorar a interface e usabilidade da ferramenta de educação chamada de Meu Texto, assim como expandir seus horizontes em questão de disseminação e abrangência de ensino. Com o projeto devidamente detalhado foi desenvolvido uma aplicação para Android com a intensão de ser uma ferramenta de auxílio aos estudos de redação. Foi aplicado um questionário para a obtenção da análise de satisfação dos usuários, sendo que como resultados uma margem positiva e satisfatória ao uso de ferramentas tecnológicas no ensino, além de uma visão de onde se pode melhorar a aplicação de maneira geral.Education has been changing in very slow steps since the beginning, when it comes to the last two decades, we have seen a great technological advance and with it a concern with the way of dated teaching that we still have. With the popularization of the internet and the dissemination of information in a quickly way and within the reach of a majority, a teaching modality is created using technology and the internet as primordial goods for an education of great quality and of complete form, as well as the possibility of sharing. This project is composed as a continuation of previous works of the Information and Communication Technology course of the Federal University of Santa Catarina (UFSC), in partnership with a master’s project by the Federal Institute of Santa Catarina (IFSC) in order to improve the interface and usability of the "Meu Texto" tool, as well as expanding its horizons in terms of dissemination and breadth of teaching. With the project in mind, an application was developed for textit Android with the intention of being a tool to aid writing studies. A questionnaire was applied to obtain the satisfaction analysis of the users, and as a result a positive and satisfactory margin for the use of technological tools in teaching, besides a vision of where one can improve the application in general way

Automated, Cost-effective, and Update-driven App Testing

Apps' pervasive role in our society led to the definition of test automation approaches to ensure their dependability. However, state-of-the-art approaches tend to generate large numbers of test inputs and are unlikely to achieve more than 50% method coverage. In this paper, we propose a strategy to achieve significantly higher coverage of the code affected by updates with a much smaller number of test inputs, thus alleviating the test oracle problem. More specifically, we present ATUA, a model-based approach that synthesizes App models with static analysis, integrates a dynamically-refined state abstraction function and combines complementary testing strategies, including (1) coverage of the model structure, (2) coverage of the App code, (3) random exploration, and (4) coverage of dependencies identified through information retrieval. Its model-based strategy enables ATUA to generate a small set of inputs that exercise only the code affected by the updates. In turn, this makes common test oracle solutions more cost-effective as they tend to involve human effort. A large empirical evaluation, conducted with 72 App versions belonging to nine popular Android Apps, has shown that ATUA is more effective and less effort intensive than state-of-the-art approaches when testing App updates

Automated Testing of Software Upgrades for Android Systems

Apps’ pervasive role in our society motivates researchers to develop automated techniques ensuring dependability through testing. However, although App updates are frequent and software engineers would like to prioritize the testing of updated features, automated testing techniques verify entire Apps and thus waste resources. Further, most testing techniques can detect only crashing failures, necessitating visual inspection of outputs to detect functional failures, which is a costly task. Despite efforts to automatically derive oracles for functional failures, the effectiveness of existing approaches is limited. Therefore, instead of automating human tasks, it seems preferable to minimize what should be visually inspected by engineers. To address the problems above, in this dissertation, we propose approaches to maximize testing effectiveness while containing test execution time and human effort. First, we present ATUA (Automated Testing of Updates for Apps), a model-based approach that synthesizes App models with static analysis, integrates a dynamically refined state abstraction function, and combines complementary testing strategies, thus enabling ATUA to generate a small set of inputs that exercise only the code affected by updates. A large empirical evaluation conducted with 72 App versions belonging to nine popular Android Apps has shown that ATUA is more effective and less effort-intensive than state-of-the-art approaches when testing App updates. Second, we present CALM (Continuous Adaptation of Learned Models), an automated App testing approach that efficiently tests App updates by adapting App models learned when automatically testing previous App versions. CALM minimizes the number of App screens to be visualized by software testers while maximizing the percentage of updated methods and instructions exercised. Our empirical evaluation shows that CALM exercises a significantly higher proportion of updated methods and instructions than baselines for the same maximum number of App screens to be visually inspected. Further, in common update scenarios, where only a small fraction of methods are updated, CALM is even quicker to outperform all competing approaches more significantly. Finally, we minimize test oracle cost by defining strategies for selecting, for visual inspection, a subset of the App outputs. We assessed 26 strategies, relying on either code coverage or action effect, on Apps affected by functional faults confirmed by their developers. Our empirical evaluation has shown that our strategies have the potential to enable the identification of a large proportion of faults. By combining code coverage with action effect, it is possible to reduce oracle cost by about 41.2% while enabling engineers to detect all the faults exercised by test automation approaches