Submitted by Nayara Passos (nayara.passos@ufpe.br) on 2015-03-13T12:46:27Z No. of bitstreams: 2 TESE Vanilson André Burégio.pdf: 5741012 bytes, checksum: a2ab30e7c8f1596c3ee84269aa93ebe7 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Approved for entry into archive by Daniella Sodre (daniella.sodre@ufpe.br) on 2015-03-13T13:20:40Z (GMT) No. of bitstreams: 2 TESE Vanilson André Burégio.pdf: 5741012 bytes, checksum: a2ab30e7c8f1596c3ee84269aa93ebe7 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) / Made available in DSpace on 2015-03-13T13:20:40Z (GMT). No. of bitstreams: 2 TESE Vanilson André Burégio.pdf: 5741012 bytes, checksum: a2ab30e7c8f1596c3ee84269aa93ebe7 (MD5) license_rdf: 1232 bytes, checksum: 66e71c371cc565284e70f40736c94386 (MD5) Previous issue date: 2014 / A abordagem aberta e distribuída da Web, bem como a prevalência de relacionamentos entre aplicações e serviços estão transformando tanto a forma como desenvolvemos software quanto como eles funcionam e interagem uns com os outros. Como resultado, uma nova geração de aplicações está emergindo e consequentemente novos modelos mentais se fazem necessários para lidar elas. Neste contexto, Máquinas Sociais aparecem como um modelo promissor para o desenvolvimento de software. Entretanto, é um tema novo, com conceitos e definições provenientes de diferentes campos de pesquisa, o que torna o entendimento unificado do conceito um esforço desafiador. Nesta tese, nós fornecemos uma base conceitual mais coerente para entender máquinas sociais como um paradigma unificado para descrever, projetar e implementar aplicações e serviços sociais emergentes. Para isso, primeiramente revisitamos o conceito de relacionamento e estendemos a noção de máquinas sociais como um modelo de abstração comum a ser utilizado para fundir elementos computacionais e sociais em software. Em segundo lugar, para descrever máquinas sociais, apresentamos diretrizes para a análise que abordam algumas questões relacionadas com o exercício de engenharia de sistemas existentes. Em terceiro lugar, definimos a Social Machine-oriented Architecture (SoMAr) - um estilo arquitetural híbrido para projetar máquinas sociais através da combinação de diferentes princípios da prática atual da engenharia de software. Por fim, discutimos as experiências e lições aprendidas com a aplicação do paradigma de máquinas sociais em diferentes contextos. / The open, distributed approach of the Web and the relationship’s prevalence of applications and services are transforming both the way we develop software and how they operate and interact with each other. As a result, a novel breed of applications is emerging, and consequently new mental models are needed to deal with them. In this context, Social Machines appear as a promising model for developing software. However, it is a fresh topic, with concepts and definitions coming from different research fields, making a unified understanding of the concept a somewhat challenging endeavor. In this thesis we provide a more coherent conceptual basis for understanding Social Machines as a unified paradigm to describe, design and implement emerging social applications and services. To do that, we revisited the concept of relationship and extend the notion of Social Machines to establish a common abstraction model that is used for blending computational and social elements into software. Second, to describe social machines, this proposal presents an analysis guideline that addresses some issues related to the engineering exercise of existing systems. Third, we provide the Social Machine-oriented Architecture (SoMAr) - a hybrid style to design social machines through the combination of different principles from current software engineering practice. Finally, we discuss the experiences and lessons learned from applying the social machines paradigm in different contexts.

Desenvolvimento Guiado por Testes (TDD) e uma das praticas sugeridas na Programacao Extrema. A mecanica da pratica e simples: o programador escreve o teste antes de escrever o codigo. E, portanto, possivel inferir que a pratica de TDD e uma pratica de testes de software. Entretanto, muitos autores de livros conhecidos pela industria e academia afirmam que os efeitos da pratica vao alem. Segundo eles, TDD ajuda o desenvolvedor durante o processo de criacao do projeto classes, fazendo-os criar classes menos acopladas e mais coesas. Entretanto, grande parte dos trabalhos da literatura sao voltados a descobrir se a pratica faz diferenca na qualidade do codigo gerado, mas poucos sao os autores que discutem como a pratica realmente auxilia. Mesmo os proprios praticantes nao entendem ou conseguem expressar bem como a pratica os guia. Este trabalho tem por objetivo compreender melhor os efeitos de TDD e como sua pratica influencia o desenvolvedor durante o processo de projeto de sistemas orientados a objetos. Para entende-las, neste trabalho optamos por um estudo exploratorio essencialmente qualitativo, no qual participantes foram convidados a resolver exercicios pre-preparados utilizando TDD e, a partir dos dados colhidos nessa primeira parte, nos levantamos detalhes sobre como a pratica influenciou as decisoes de projeto de classes dos participantes por meio de entrevistas. Ao final, observamos que a pratica de TDD pode guiar o desenvolvedor durante o processo de criacao do projeto de classes por meio de constantes feedbacks sobre a qualidade do projeto. Esses feedbacks alertam desenvolvedores sobre possiveis problemas, como alto acoplamento ou baixa coesao. Os desenvolvedores, por sua vez, devem interpretar e melhorar o projeto de classes. Este trabalho catalogou e nomeou os padroes de feedback percebidos pelos participantes. / Test-Driven Development (TDD) is one of the suggested practices in Extreme Programming (XP). The mechanical is simple: the developer writes a test before writing the implementation. Thus, TDD is often seen as a software testing technique. However, many famous book authors suggest that TDD can help developers during the class design creation process, enabling developers to create less coupled highly cohesive classes. Most of the academic studies are interested on finding the difference between a TDDd and a non-TDDd code. Only a few of them discuss how the practice really supports class design. Even practitioners do not understand how the practice guides them. This work aims to understand better the effects of TDD and how the practice influences the practitioner during the class design process in object-oriented systems. To better understand them, we did a essencially qualitative explorative study, in which participants were invited to solve a set of pre-prepared exercises using TDD and, based on the gathered data, we retrieved details of how the practice influenced the developers class design decisions through interviews. At the end, we observed that the practice of TDD can guide developers during the class design creation process through constant feedback about its quality. These feedbacks alert developers about possible problems, such as high coupling or low cohesion. Developers then should interpret and improve the class design accordingly. This study also catalogues the TDD feedback patterns perceived by the participants.

The Object-Oriented Data Specification Framework (OODSF) is a C++ framework to facilitate programming in a heterogeneous distributed environment. Using the OODSF, C++ language bindings of commonly used specification languages, such as Abstract Syntax Notation 1 (ASN.l) and Interface Definition Language (IDL), can be defined. The OODSF defines C++ class libraries for ASN.l and IDL to simplify the C++ language bindings. Arbitrary application-level IDL and ASN.l specifications can be translated into C++ representations based on these class libraries. The OODSF contains facilities for encoding and decoding transferred data, allowing interoperability in a heterogeneous distributed system. A general interface is provided to encoding and decoding services so that a flexible choice of an encoding rule can be made. The current implementation of the OODSF contains external Encoding Rule (XDR) and Basic Encoding Rule (BER). / Master of Science

"This research is aimed at studying the key technologies of Computer-Aided Manufacturing Planning (CAMP) of mass customization for non-rotational part production. The main goal of the CAMP is to rapidly generate manufacturing plans by using of the best-of-practice (BOP) provided by specific companies. A systematic information modeling hierarchy is proposed to facilitate changes in manufacturing plans according to changes in part design. The Object-oriented Systems Analysis (OSA) approach is used to represent information relationships and associativities in the CAMP. A feature-based part information model, a process model, a setup planning model, and manufacturing resource capability models are established. A three-level decision-making mechanism is proposed for the CAMP. At the feature- level, combined features are defined based on part families, and a process model is proposed to describe the information associativities between features and their manufacturing strategies, which include customized cutters and toolpaths. At the part level, graph-based setup planning is carried out by tolerance analysis and manufacturing resource capability analysis. At the machine level, multi-part fixtures are utilized to pursue high productivity. Cycle time is used to evaluate manufacturing plans. Computer software for the CAMP has been developed and integrated with CAD package Unigraphs. The BOP of part families is stored in XML format, which has good extendibility and can be read and edited by standard browsers."

<p>For researches in the field of the data mining and machine learning the necessary condition is an availability of various input data set. Now researchers create the databases of such sets. Examples of the following systems are: The UCI Machine Learning Repository, Data Envelopment Analysis Dataset Repository, XMLData Repository, Frequent Itemset Mining Dataset Repository. Along with above specified statistical repositories, the whole pleiad from simple filestores to specialized repositories can be used by researchers during solution of applied tasks, researches of own algorithms and scientific problems. It would seem, a single complexity for the user will be search and direct understanding of structure of so separated storages of the information. However detailed research of such repositories leads us to comprehension of deeper problems existing in usage of data. In particular a complete mismatch and rigidity of data files structure with SDMX - Statistical Data and Metadata Exchange - standard and structure used by many European organizations, impossibility of preliminary data origination to the concrete applied task, lack of data usage history for those or other scientific and applied tasks.</p><p>Now there are lots of methods of data miming, as well as quantities of data stored in various repositories. In repositories there are no methods of DM (data miming) and moreover, methods are not linked to application areas. An essential problem is subject domain link (problem domain), methods of DM and datasets for an appropriate method. Therefore in this work we consider the building problem of ontological models of DM methods, interaction description of methods of data corresponding to them from repositories and intelligent agents allowing the statistical repository user to choose the appropriate method and data corresponding to the solved task. In this work the system structure is offered, the intelligent search agent on ontological model of DM methods considering the personal inquiries of the user is realized.</p><p>For implementation of an intelligent data and metadata exchange repository the agent oriented approach has been selected. The model uses the service oriented architecture. Here is used the cross platform programming language Java, multi-agent platform Jadex, database server Oracle Spatial 10g, and also the development environment for ontological models - Protégé Version 3.4.</p>

For researches in the field of the data mining and machine learning the necessary condition is an availability of various input data set. Now researchers create the databases of such sets. Examples of the following systems are: The UCI Machine Learning Repository, Data Envelopment Analysis Dataset Repository, XMLData Repository, Frequent Itemset Mining Dataset Repository. Along with above specified statistical repositories, the whole pleiad from simple filestores to specialized repositories can be used by researchers during solution of applied tasks, researches of own algorithms and scientific problems. It would seem, a single complexity for the user will be search and direct understanding of structure of so separated storages of the information. However detailed research of such repositories leads us to comprehension of deeper problems existing in usage of data. In particular a complete mismatch and rigidity of data files structure with SDMX - Statistical Data and Metadata Exchange - standard and structure used by many European organizations, impossibility of preliminary data origination to the concrete applied task, lack of data usage history for those or other scientific and applied tasks. Now there are lots of methods of data miming, as well as quantities of data stored in various repositories. In repositories there are no methods of DM (data miming) and moreover, methods are not linked to application areas. An essential problem is subject domain link (problem domain), methods of DM and datasets for an appropriate method. Therefore in this work we consider the building problem of ontological models of DM methods, interaction description of methods of data corresponding to them from repositories and intelligent agents allowing the statistical repository user to choose the appropriate method and data corresponding to the solved task. In this work the system structure is offered, the intelligent search agent on ontological model of DM methods considering the personal inquiries of the user is realized. For implementation of an intelligent data and metadata exchange repository the agent oriented approach has been selected. The model uses the service oriented architecture. Here is used the cross platform programming language Java, multi-agent platform Jadex, database server Oracle Spatial 10g, and also the development environment for ontological models - Protégé Version 3.4.

Feature-based formalisms provide an elegant way to specify families of systems that share a base functionality and differ in certain features. They can also facilitate an all-in-one analysis, where all systems of the family are analyzed at once on a single family model instead of one-by-one. This paper presents the basic concepts of the tool ProFeat, which provides a guarded-command language for modeling families of probabilistic systems and an automatic translation of family models to the input language of the probabilistic model checker PRISM. This translational approach enables a family-based quantitative analysis with PRISM. Besides modeling families of systems that differ in system parameters such as the number of identical processes or channel sizes, ProFeat also provides special support for the modeling and analysis of (probabilistic) product lines with dynamic feature switches, multi-features and feature attributes. By means of several case studies we show how ProFeat eases family-based modeling and compare the one-by-one and all-in-one analysis approach.

The concept of features provides an elegant way to specify families of systems. Given a base system, features encapsulate additional functionalities that can be activated or deactivated to enhance or restrict the base system’s behaviors. Features can also facilitate the analysis of families of systems by exploiting commonalities of the family members and performing an all-in-one analysis, where all systems of the family are analyzed at once on a single family model instead of one-by-one. Most prominent, the concept of features has been successfully applied to describe and analyze (software) product lines. We present the tool ProFeat that supports the feature-oriented engineering process for stochastic systems by probabilistic model checking. To describe families of stochastic systems, ProFeat extends models for the prominent probabilistic model checker Prism by feature-oriented concepts, including support for probabilistic product lines with dynamic feature switches, multi-features and feature attributes. ProFeat provides a compact symbolic representation of the analysis results for each family member obtained by Prism to support, e.g., model repair or refinement during feature-oriented development. By means of several case studies we show how ProFeat eases family-based quantitative analysis and compare one-by-one and all-in-one analysis approaches.