A Comparative Study of GUI Testing Approaches

A maioria das aplicações de software modernas apresentam uma Interface Gráfica de Utilizador (GUI), que torna a aplicação mais simples de usar, promovendo maior produtividade e melhor acessibilidade, e oferecendo flexibilidade na forma como os utilizadores podem executar tarefas. No entanto, devido à complexidade das GUIs, o processo de teste de GUI pode ser moroso e intensivo. Assim, automatizar o processo tanto quanto possível é indispensável para testar qualquer interface gráfica mais complexa e evoluída.Existem diferentes abordagens para testes de GUI automatizados, no entanto, a maioria delas requerer esforços manuais substanciais, outras apenas são capazes de encontrar erros específicos ou não permitem a reutilização de casos de teste após alterações de sistema ou GUI. Muitos investigadores afirmam que devem ser utilizadas diferentes técnicas/abordagens para um bom processo de teste.Uma nova abordagem baseada em modelos, denominada de Pattern-Based GUI Testing, foi implementada a fim de aumentar a sistematização, reutilização e diminuir o esforço da modelação e teste de GUIs. Baseia-se no conceito de Padrões de Teste de Interface de Utilizador (UITP), que contêm estratégias de teste genéricas para testar características recorrentes e comuns (UI Patterns) em GUIs. É apoiada pela ferramenta PBGT, que integra um ambiente de modelação e execução de testes de modo a suportar a criação de modelos de teste com base em UITPs, com recurso a uma linguagem específica de domínio (PARADIGM) para modelação da GUI.Como a abordagem é recente, é relevante submetê-la a experiências e testes sistematizados a fim de avaliar o seu bom desempenho/comportamento e compará-la com outras técnicas. Assim, este trabalho de dissertação baseia-se na avaliação e comparação da abordagem PBGT em relação a outras ferramentas e técnicas, no que diz respeito à detecção de falhas, facilidade de utilização, e aos esforços necessários para testar a aplicação.Para a realização de experiências, foram introduzidas mutações em três aplicações web diferentes - iAddressBook , TaskFreak e Tudu - de modo a abranger um maior número de casos de uso, e cada mutante foi, por sua vez, testado por cada uma das ferramentas selecionadas ou desenvolvidas e que implementam as diferentes abordagens de teste consideradas.Most of the modern software applications feature a Graphical User Interface (GUI), which turns the application easier to use, promoting higher productivity and better accessibility, and offering flexibility in how users perform tasks. However, due to GUI's complexity, the GUI testing process can be a time-consuming and intensive process. Therefore, automate the process as much as possible is indispensable to test any more evolved graphic user interface.There are some common automated GUI testing approaches, but while most of them require substantial manual efforts, others lack reusability or are only able to find specific types of errors. Many researchers state that a variety of techniques should be used.A new model-based testing approach, called Pattern- Based GUI Testing, was implemented in order to increase systematization, reusability and diminish the effort in modelling and testing. It is based on the concept of User Interface Test Patterns (UITP), which contain generic test strategies for testing common recurrent behavior (UI Patterns) on GUIs, and supported by the PBGT Tool which provides an integrated modeling and testing environment that supports the crafting of test models based on UI Test Patterns, using a GUI modeling DSL (PARADIGM).As a novel proposal, it is entirely relevant to submit it to systematized experiments and tests in order to assess its good performance/behavior and compare it with other techniques. Thus, this dissertation work mainly addresses PBGT's approach, aiming to compare it with different testing approaches/tools in what concerns to errors/fault detection, ease of use, and overall efforts required to test the application.To perform the experiments, mutations were introduced in each of three different web applications - iAddressBook, TaskFreak and Tudu - to cover a greater number of use cases, and each mutant was tested by each of the selected or developed testing tools which implement the considered approaches. Those approaches' benefits and problems are then conveniently described

Teaching Concurrent Software Design: A Case Study Using Android

In this article, we explore various parallel and distributed computing topics from a user-centric software engineering perspective. Specifically, in the context of mobile application development, we study the basic building blocks of interactive applications in the form of events, timers, and asynchronous activities, along with related software modeling, architecture, and design topics.Comment: Submitted to CDER NSF/IEEE-TCPP Curriculum Initiative on Parallel and Distributed Computing - Core Topics for Undergraduate

Phase and Amplitude Interferometry Based Radio Frequency Direction Finder

Direction finding (DF) systems have been around for decades, preceding WWII. The main function of these systems is to calculate the direction of arrival of an electromagnetic wave. There are many real-world applications which utilize direction finders and direction-finding techniques, from recreational “fox hunts” to military geolocation systems. The following approach for implementing a direction finding system revolves around the phase and amplitude of a signal that is being radiated at an unlicensed frequency of 2.45Ghz by an RF source. The system is comprised of an antenna array of 4 antennas which can be used receive the radiated signal. By comparing the amplitudes of the signal received by each antenna relative to each other, the quadrant from which the RF source is located in can be identified. By comparing the phase difference, 0° to +/- 180°, of the signal received by each antenna relative to each other, four possible directions can be calculated, one in each quadrant. Using the information discovered from comparing the phase and the amplitudes of the received signal at each antenna, the direction of the RF source can be found. The system runs the direction finding algorithm when the user commands it to from the graphical user interface (GUI), iterates it hundreds of times per second, and averages the found direction to reduce the effects of noise. The direction is then displayed on the GUI

MITK-ModelFit: A generic open-source framework for model fits and their exploration in medical imaging -- design, implementation and application on the example of DCE-MRI

Many medical imaging techniques utilize fitting approaches for quantitative parameter estimation and analysis. Common examples are pharmacokinetic modeling in DCE MRI/CT, ADC calculations and IVIM modeling in diffusion-weighted MRI and Z-spectra analysis in chemical exchange saturation transfer MRI. Most available software tools are limited to a special purpose and do not allow for own developments and extensions. Furthermore, they are mostly designed as stand-alone solutions using external frameworks and thus cannot be easily incorporated natively in the analysis workflow. We present a framework for medical image fitting tasks that is included in MITK, following a rigorous open-source, well-integrated and operating system independent policy. Software engineering-wise, the local models, the fitting infrastructure and the results representation are abstracted and thus can be easily adapted to any model fitting task on image data, independent of image modality or model. Several ready-to-use libraries for model fitting and use-cases, including fit evaluation and visualization, were implemented. Their embedding into MITK allows for easy data loading, pre- and post-processing and thus a natural inclusion of model fitting into an overarching workflow. As an example, we present a comprehensive set of plug-ins for the analysis of DCE MRI data, which we validated on existing and novel digital phantoms, yielding competitive deviations between fit and ground truth. Providing a very flexible environment, our software mainly addresses developers of medical imaging software that includes model fitting algorithms and tools. Additionally, the framework is of high interest to users in the domain of perfusion MRI, as it offers feature-rich, freely available, validated tools to perform pharmacokinetic analysis on DCE MRI data, with both interactive and automatized batch processing workflows.Comment: 31 pages, 11 figures URL: http://mitk.org/wiki/MITK-ModelFi

Overcoming Language Dichotomies: Toward Effective Program Comprehension for Mobile App Development

Mobile devices and platforms have become an established target for modern software developers due to performant hardware and a large and growing user base numbering in the billions. Despite their popularity, the software development process for mobile apps comes with a set of unique, domain-specific challenges rooted in program comprehension. Many of these challenges stem from developer difficulties in reasoning about different representations of a program, a phenomenon we define as a "language dichotomy". In this paper, we reflect upon the various language dichotomies that contribute to open problems in program comprehension and development for mobile apps. Furthermore, to help guide the research community towards effective solutions for these problems, we provide a roadmap of directions for future work.Comment: Invited Keynote Paper for the 26th IEEE/ACM International Conference on Program Comprehension (ICPC'18

A traffic classification method using machine learning algorithm

Applying concepts of attack investigation in IT industry, this idea has been developed to design a Traffic Classification Method using Data Mining techniques at the intersection of Machine Learning Algorithm, Which will classify the normal and malicious traffic. This classification will help to learn about the unknown attacks faced by IT industry. The notion of traffic classification is not a new concept; plenty of work has been done to classify the network traffic for heterogeneous application nowadays. Existing techniques such as (payload based, port based and statistical based) have their own pros and cons which will be discussed in this literature later, but classification using Machine Learning techniques is still an open field to explore and has provided very promising results up till now