In software development process, there can be misunderstandings, communication problems between individual members of the development team and mistakes when writing code or designing applications. These problems need to be resolved before the application is released into the production environment. To discover these errors it is necessary to properly test the software product. All these activities are engaged in discipline called Software Quality Assurance (SQA), which is an integral part of the software development process. The thesis discusses this discipline and procedures for software testing using the Microsoft Visual Studio 2008. The main objective of this work is to create comprehensive methodology for automated testing of web and Windows applications in Microsoft Visual Studio and to verify that procedures in practice. The reader will learn the theoretical basis of software testing at the beginning of the thesis. In next chapter, there are described various features that Microsoft Visual Studio 2008 offers for software testing. Following chapter describes the applications testing in IBM Rational Functional Tester, which is suitable for Windows applications testing (Microsoft Visual Studio 2008 does not support this kind of tests). In other part of the work -- Case Study -- the reader will learn web and Windows applications testing. Case study can serve as a basis for further methodological manual for teams engaged in software testing. The usage of instruments for testing is explained in real examples. At the end of the work there are set out recommendations for the team collaboration using Microsoft Team Foundation Server, and there are also compared the testing instruments and reviewed functions that will come up with Microsoft Visual Studio 2010.

The Open Source Software Development (OSSD) model has launched products in rapid succession and with high quality, without following traditional quality practices of accepted software development models (Raymond 1999). Some OSSD projects challenge established quality assurance approaches, claiming to be successful through partial contrary techniques of standard software development. However, empirical studies of quality assurance practices for Open Source Software (OSS) are rare (Glass 2001). Therefore, further research is required to evaluate the quality assurance processes and methods within the OSSD model. The aim of this research is to improve the understanding of quality assurance practices under the OSSD model. The OSSD model is characterised by a collaborative, distributed development approach with public communication, free participation, free entry to the project for newcomers and unlimited access to the source code. The research examines applied quality assurance practices from a process view rather than from a product view. The research follows ideographic and nomothetic methodologies and adopts an antipositivist epistemological approach. An empirical research of applied quality assurance practices in OSS projects is conducted through the literature research. The survey research method is used to gain empirical evidence about applied practices. The findings are used to validate the theoretical knowledge and to obtain further expertise about practical approaches. The findings contribute to the development of a quality assurance framework for standard OSSD approaches. The result is an appropriate quality model with metrics that the requirements of the OSSD support. An ideographic approach with case studies is used to extend the body of knowledge and to assess the feasibility and applicability of the quality assurance framework. In conclusion, the study provides further understanding of the applied quality assurance processes under the OSSD model and shows how a quality assurance framework can support the development processes with guidelines and measurements.

Software test automation is widely accepted as an efficient software testing technique. However, automation has failed to deliver the expected productivity more often than not. The goal of this research was to find out the reason for these failures by collecting and understanding the metrics that affect software test automation and provide recommendations on how to successfully adopt automation with a positive return on investment (ROI). The metrics of concern were schedule, cost and effectiveness. The research employed an experimental study where subjects worked on individual manual and automated testing projects. The data collected were cross verified and supplemented with additional data from a feedback survey at the end of the experiment. The results of this study suggest that automation involves a heavy initial investment in terms of schedule and cost, which needs to be amortized over subsequent test cycles or even subsequent test projects.

The subject of this thesis is testing and quality assurance during software development. The theoretical part explains the meaning of software quality and then describes the metrics used to evaluate software quality. The following part explains the differences between software quality assurance in agile and traditional software development methodologies, including criteria on how to compare the methodologies. Throughout the thesis, there are briefly summarized basic concepts which then include the differences between stat-ic/dynamic testing and manual/automatic testing, as well as a role of quality assurance en-gineer in software development. The practical section extends to an existing software development methodology for small software projects (MMSP) in its testing area. New testing activities, artifacts, and roles are introduced in order to align with real requirements for software testing. They will also function in the methodology when used in the testing area for development of more robust applications in bigger teams. Test management tools and test automation tools are described and followed with recommendations for methodol-ogy usage for only a selected few.

The subject of this thesis is web applications quality assurance using automated testing tools. The main goal of this thesis is design and implementation solution for automated regression testing of map web application. In the first chapter are described principles of software quality assurance focused to software testing. After that follows chapter about the possibility of automatic software testing based on analysis tools available to support automated testing of web applications. Next chapters of this thesis are devoted to choosing the ideal tool of automated testing, design and implementation of solutions for automated regression testing of web map application.

The focus of the dissertation is on software quality assurance for object-oriented information systems development. A Quality Assurance Reference Model is proposed with aspects dealing with technical and managerial issues. A revised Spiral life cycle model is adopted as well as the Object Modelling Technique. The Quality Assurance Reference Model associates quality factors at various levels, quality criteria and metrics into a matrix framework that may be used to achieve quality assurance for all cycles of the Spiral Model. / Computing / M. Sc. (Information Systems)

Defect prediction models assist tester practitioners on prioritizing the most defect-prone parts of the software. The approach called Cross-Project Defect Prediction (CPDP) refers to the use of known external projects to compose the training set. This approach is useful when the amount of historical defect data of a company to compose the training set is inappropriate or insufficient. Although the principle is attractive, the predictive performance is a limiting factor. In recent years, several methods were proposed aiming at improving the predictive performance of CPDP models. However, to the best of our knowledge, there is no evidence of which CPDP methods typically perform best. Moreover, there is no evidence on which CPDP methods perform better for a specific application domain. In fact, there is no machine learning algorithm suitable for all domains. The decision task of selecting an appropriate algorithm for a given application domain is investigated in the meta-learning literature. A meta-learning model is characterized by its capacity of learning from previous experiences and adapting its inductive bias dynamically according to the target domain. In this work, we investigate the feasibility of using meta-learning for the recommendation of CPDP methods. In this thesis, three main goals were pursued. First, we provide an experimental analysis to investigate the feasibility of using Feature Selection (FS) methods as an internal procedure to improve the performance of two specific CPDP methods. Second, we investigate which CPDP methods present typically best performances. We also investigate whether the typically best methods perform best for the same project datasets. The results reveal that the most suitable CPDP method for a project can vary according to the project characteristics, which leads to the third investigation of this work. We investigate the several particularities inherent to the CPDP context and propose a meta-learning solution able to learn from previous experiences and recommend a suitable CDPD method according to the characteristics of the project being predicted. We evaluate the learning capacity of the proposed solution and its performance in relation to the typically best CPDP methods. / Modelos de predição de defeitos auxiliam profissionais de teste na priorização de partes do software mais propensas a conter defeitos. A abordagem de predição de defeitos cruzada entre projetos (CPDP) refere-se à utilização de projetos externos já conhecidos para compor o conjunto de treinamento. Essa abordagem é útil quando a quantidade de dados históricos de defeitos é inapropriada ou insuficiente para compor o conjunto de treinamento. Embora o princípio seja atrativo, o desempenho de predição é um fator limitante nessa abordagem. Nos últimos anos, vários métodos foram propostos com o intuito de melhorar o desempenho de predição de modelos CPDP. Contudo, na literatura, existe uma carência de estudos comparativos que apontam quais métodos CPDP apresentam melhores desempenhos. Além disso, não há evidências sobre quais métodos CPDP apresentam melhor desempenho para um domínio de aplicação específico. De fato, não existe um algoritmo de aprendizado de máquina que seja apropriado para todos os domínios de aplicação. A tarefa de decisão sobre qual algoritmo é mais adequado a um determinado domínio de aplicação é investigado na literatura de meta-aprendizado. Um modelo de meta-aprendizado é caracterizado pela sua capacidade de aprender a partir de experiências anteriores e adaptar seu viés de indução dinamicamente de acordo com o domínio alvo. Neste trabalho, nós investigamos a viabilidade de usar meta-aprendizado para a recomendação de métodos CPDP. Nesta tese são almejados três principais objetivos. Primeiro, é conduzida uma análise experimental para investigar a viabilidade de usar métodos de seleção de atributos como procedimento interno de dois métodos CPDP, com o intuito de melhorar o desempenho de predição. Segundo, são investigados quais métodos CPDP apresentam um melhor desempenho em um contexto geral. Nesse contexto, também é investigado se os métodos com melhor desempenho geral apresentam melhor desempenho para os mesmos conjuntos de dados (ou projetos de software). Os resultados revelam que os métodos CPDP mais adequados para um projeto podem variar de acordo com as características do projeto sendo predito. Essa constatação conduz à terceira investigação realizada neste trabalho. Foram investigadas as várias particularidades inerentes ao contexto CPDP a fim de propor uma solução de meta-aprendizado capaz de aprender com experiências anteriores e recomendar métodos CPDP adequados, de acordo com as características do software. Foram avaliados a capacidade de meta-aprendizado da solução proposta e a sua performance em relação aos métodos base que apresentaram melhor desempenho geral.

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The focus of the dissertation is on software quality assurance for object-oriented information systems development. A Quality Assurance Reference Model is proposed with aspects dealing with technical and managerial issues. A revised Spiral life cycle model is adopted as well as the Object Modelling Technique. The Quality Assurance Reference Model associates quality factors at various levels, quality criteria and metrics into a matrix framework that may be used to achieve quality assurance for all cycles of the Spiral Model. / Computing / M. Sc. (Information Systems)

Defect prediction models assist tester practitioners on prioritizing the most defect-prone parts of the software. The approach called Cross-Project Defect Prediction (CPDP) refers to the use of known external projects to compose the training set. This approach is useful when the amount of historical defect data of a company to compose the training set is inappropriate or insufficient. Although the principle is attractive, the predictive performance is a limiting factor. In recent years, several methods were proposed aiming at improving the predictive performance of CPDP models. However, to the best of our knowledge, there is no evidence of which CPDP methods typically perform best. Moreover, there is no evidence on which CPDP methods perform better for a specific application domain. In fact, there is no machine learning algorithm suitable for all domains. The decision task of selecting an appropriate algorithm for a given application domain is investigated in the meta-learning literature. A meta-learning model is characterized by its capacity of learning from previous experiences and adapting its inductive bias dynamically according to the target domain. In this work, we investigate the feasibility of using meta-learning for the recommendation of CPDP methods. In this thesis, three main goals were pursued. First, we provide an experimental analysis to investigate the feasibility of using Feature Selection (FS) methods as an internal procedure to improve the performance of two specific CPDP methods. Second, we investigate which CPDP methods present typically best performances. We also investigate whether the typically best methods perform best for the same project datasets. The results reveal that the most suitable CPDP method for a project can vary according to the project characteristics, which leads to the third investigation of this work. We investigate the several particularities inherent to the CPDP context and propose a meta-learning solution able to learn from previous experiences and recommend a suitable CDPD method according to the characteristics of the project being predicted. We evaluate the learning capacity of the proposed solution and its performance in relation to the typically best CPDP methods. / Modelos de predição de defeitos auxiliam profissionais de teste na priorização de partes do software mais propensas a conter defeitos. A abordagem de predição de defeitos cruzada entre projetos (CPDP) refere-se à utilização de projetos externos já conhecidos para compor o conjunto de treinamento. Essa abordagem é útil quando a quantidade de dados históricos de defeitos é inapropriada ou insuficiente para compor o conjunto de treinamento. Embora o princípio seja atrativo, o desempenho de predição é um fator limitante nessa abordagem. Nos últimos anos, vários métodos foram propostos com o intuito de melhorar o desempenho de predição de modelos CPDP. Contudo, na literatura, existe uma carência de estudos comparativos que apontam quais métodos CPDP apresentam melhores desempenhos. Além disso, não há evidências sobre quais métodos CPDP apresentam melhor desempenho para um domínio de aplicação específico. De fato, não existe um algoritmo de aprendizado de máquina que seja apropriado para todos os domínios de aplicação. A tarefa de decisão sobre qual algoritmo é mais adequado a um determinado domínio de aplicação é investigado na literatura de meta-aprendizado. Um modelo de meta-aprendizado é caracterizado pela sua capacidade de aprender a partir de experiências anteriores e adaptar seu viés de indução dinamicamente de acordo com o domínio alvo. Neste trabalho, nós investigamos a viabilidade de usar meta-aprendizado para a recomendação de métodos CPDP. Nesta tese são almejados três principais objetivos. Primeiro, é conduzida uma análise experimental para investigar a viabilidade de usar métodos de seleção de atributos como procedimento interno de dois métodos CPDP, com o intuito de melhorar o desempenho de predição. Segundo, são investigados quais métodos CPDP apresentam um melhor desempenho em um contexto geral. Nesse contexto, também é investigado se os métodos com melhor desempenho geral apresentam melhor desempenho para os mesmos conjuntos de dados (ou projetos de software). Os resultados revelam que os métodos CPDP mais adequados para um projeto podem variar de acordo com as características do projeto sendo predito. Essa constatação conduz à terceira investigação realizada neste trabalho. Foram investigadas as várias particularidades inerentes ao contexto CPDP a fim de propor uma solução de meta-aprendizado capaz de aprender com experiências anteriores e recomendar métodos CPDP adequados, de acordo com as características do software. Foram avaliados a capacidade de meta-aprendizado da solução proposta e a sua performance em relação aos métodos base que apresentaram melhor desempenho geral.