As atividades de teste de software são de crescente importância devido à maciça presença de sistemas de informação em nosso cotidiano. Programas de Processamento de Imagens (PI) têm um domínio de entrada bastante complexo e, por essa razão, o teste tradicional realizado com esse tipo de programa, conduzido majoritariamente de forma manual, é uma tarefa de alto custo e sujeita a imperfeições. No teste tradicional, em geral, as imagens de entrada são construídas manualmente pelo testador ou selecionadas aleatoriamente de bases de imagens, muitas vezes dificultando a revelação de defeitos no software. A partir de um mapeamento sistemático da literatura realizado, foi identificada uma lacuna no que se refere à geração automatizada de dados de teste no domínio de imagens. Assim, o objetivo desta pesquisa é propor uma abordagem - denominada TAIGA (Test imAge generatIon by Genetic Algorithm) - para a geração de dados de teste para programas de PI por meio de algoritmo genético. Na abordagem proposta, operadores genéticos tradicionais (mutação e crossover) são adaptados para o domínio de imagens e a função fitness é substituída por uma avaliação de resultados provenientes de teste de mutação. A abordagem TAIGA foi validada por meio de experimentos com oito programas de PI distintos, nos quais observaram-se ganhos de até 38,61% em termos de mutation score em comparação ao teste tradicional. Ao automatizar a geração de dados de teste, espera-se conferir maior qualidade ao desenvolvimento de sistemas de PI e contribuir com a diminuição de custos com as atividades de teste de software neste domínio / The massive presence of information systems in our lives has been increasing the importance of software test activities. Image Processing (IP) programs have very complex input domains and, therefore, the traditional testing for this kind of program is a highly costly and vulnerable to errors task. In traditional testing, usually, testers create images by themselves or they execute random selection from images databases, which can make it harder to reveal faults in the software under test. In this context, a systematic mapping study was conducted and a gap was identified concerning the automated test data generation in the images domain. Thus, an approach for generating test data for IP programs by means of genetic algorithms was proposed: TAIGA - Test imAge generatIon by Genetic Algorithm. This approach adapts traditional genetic operators (mutation and crossover) to the images domain and replaces the fitness function by the evaluation of the results of mutation testing. The proposed approach was validated by the execution of experiments involving eight distinct IP programs. TAIGA was able to provide up to 38.61% increase in mutation score when compared to the traditional testing for IP programs. It\'s expected that the automation of test data generation elevates the quality of image processing systems development and reduces the costs of software test activities in the images domain

As atividades de teste de software são de crescente importância devido à maciça presença de sistemas de informação em nosso cotidiano. Programas de Processamento de Imagens (PI) têm um domínio de entrada bastante complexo e, por essa razão, o teste tradicional realizado com esse tipo de programa, conduzido majoritariamente de forma manual, é uma tarefa de alto custo e sujeita a imperfeições. No teste tradicional, em geral, as imagens de entrada são construídas manualmente pelo testador ou selecionadas aleatoriamente de bases de imagens, muitas vezes dificultando a revelação de defeitos no software. A partir de um mapeamento sistemático da literatura realizado, foi identificada uma lacuna no que se refere à geração automatizada de dados de teste no domínio de imagens. Assim, o objetivo desta pesquisa é propor uma abordagem - denominada TAIGA (Test imAge generatIon by Genetic Algorithm) - para a geração de dados de teste para programas de PI por meio de algoritmo genético. Na abordagem proposta, operadores genéticos tradicionais (mutação e crossover) são adaptados para o domínio de imagens e a função fitness é substituída por uma avaliação de resultados provenientes de teste de mutação. A abordagem TAIGA foi validada por meio de experimentos com oito programas de PI distintos, nos quais observaram-se ganhos de até 38,61% em termos de mutation score em comparação ao teste tradicional. Ao automatizar a geração de dados de teste, espera-se conferir maior qualidade ao desenvolvimento de sistemas de PI e contribuir com a diminuição de custos com as atividades de teste de software neste domínio / The massive presence of information systems in our lives has been increasing the importance of software test activities. Image Processing (IP) programs have very complex input domains and, therefore, the traditional testing for this kind of program is a highly costly and vulnerable to errors task. In traditional testing, usually, testers create images by themselves or they execute random selection from images databases, which can make it harder to reveal faults in the software under test. In this context, a systematic mapping study was conducted and a gap was identified concerning the automated test data generation in the images domain. Thus, an approach for generating test data for IP programs by means of genetic algorithms was proposed: TAIGA - Test imAge generatIon by Genetic Algorithm. This approach adapts traditional genetic operators (mutation and crossover) to the images domain and replaces the fitness function by the evaluation of the results of mutation testing. The proposed approach was validated by the execution of experiments involving eight distinct IP programs. TAIGA was able to provide up to 38.61% increase in mutation score when compared to the traditional testing for IP programs. It\'s expected that the automation of test data generation elevates the quality of image processing systems development and reduces the costs of software test activities in the images domain

The software industry is expanding at a rapid rate. To keep up with the fast-growing and ever-changing technologies, it has become necessary to produce high-quality software in a short time and at an affordable cost. This research aims to demonstrate to Java developers the use of Automated Test Case Generation (ATCG) tools by presenting the tools that exist today, their usefulness, and their effectiveness. The main focus is on the automated testing tools for the Java industry, which can help developers achieve their goals faster and make better software. Moreover, the discussion covers the availability, features, prerequisites, effectiveness, and limitations of the automated testing tools. Among these tools, the most widely used are Evosuite, JUnit, TestNG, and Selenium. Each tool has its advantages and purpose. Furthermore, these ATCG-tools were compared to provide a clear picture to Java developers, answer the research questions, and show strengths and limitations of each selected tool. Results show that there is no single ultimate tool that can do all kinds of testing independently. It all depends on what the developer aims to achieve. If one tool is good at generating unit test cases for Java classes, another tool is good at testing the code security through penetration testing. Therefore, the Java developers may choose a tool/s based on their requirements. This study has revealed captivating findings regarding the ATCG-tools, which ought to be explored in the future.