Uma Linha de Produtos de Software (LPS) consiste de um conjunto de sistemas de software que compartilham características comuns e satisfazem às necessidades específicas de um segmento particular. Para tornar o processo de instanciação de produtos mais rápido e menos suscetível a erros, o projeto de uma LPS pode adotar a utilização de geradores de aplicação, que podem gerar os artefatos da LPS utilizando uma especificação das variabilidades de um certo produto. Adicionalmente, notase que determinadas características transversais de uma linha de produtos têm potencial de reúso em diferentes domínios, podendo ser implementadas usando a programação orientada a aspectos (POA). Neste trabalho é proposto um processo para o desenvolvimento de LPS e geração automatizada de produtos levando em consideração os interesses transversais existentes em cada domínio de aplicação. Os interesses transversais são as características comuns espalhadas pelas divisões ou módulos do programa de diferentes domínios. O processo aqui proposto tem a finalidade de aumentar o reúso de características de linhas de produtos por meio da POA, permitindo que as LPSs sejam projetadas de forma mais coesa e, consequentemente, facilitando sua manutenção e evolução. Visando diminuir o esforço necessário para a instanciação dos produtos provenientes dessas linhas de produtos, neste trabalho também é apresentada uma extensão do gerador Captor, denominada Captor-AO. Esse gerador fornece suporte ao processo proposto, permitindo a criação de produtos formados por características de diferentes domínios. Por fim, é apresentado um estudo de caso em que é realizada a configuração de um domínio transversal para o interesse de persistência, a definição de um domínio-base compatível com esse domínio transversal e a geração de produtos formados pelas características de ambos os domínios utilizando o gerador estendido Captor-AO / A Software Product Line (SPL) consists of a set of software systems that share common features and fulfill the specific requirements of a particular domain. In order to make the products instantiation process faster and less prone to errors, the project of a SPL can adopt the utilization of application generators, which can can automatically generate the SPL artifacts based on the specification of the variabilities of a particular product. Additionally, it can be noticed that certain crosscutting features of a product line have potencial to be reused in different domains, so they can be implemented using aspect oriented programming (AOP). In this work, a process is proposed for the development of SPLs and automatic generation of products, considering the crosscutting concerns present in each application domain. The crosscutting concerns are related to the common features that are scattered around program divisions or modules of different domains. The process proposed here has the goal of enhancing the reuse of SPL features using AOP, allowing the design of SPL in a more cohesive way and, thus, easing its maintenance and evolution. Aiming at decreasing the effort needed to instantiate products from these SPL, this work also presents an extension to the Captor application generator, named Captor-AO. This generator supports the proposed process, allowing the creation of products composed by features of different domains. Finally, a case study is presented in which Captor-AO is configured with two domains: a crosscutting domain for the persistence concern and a base domain compatible with this crosscutting domain, such that the generation of products can be done by composing features of both domains

<p>This thesis presents the development of a prototype of the Baseline Oriented Modeling</p><p>(BOM) approach, called protoBOM.</p><p>BOM is a framework that semi-automatically generates Decision Support Systems in a</p><p>specific domain, based on Software Product Lines.</p><p>protoBOM semi-automatically generates applications as PRISMA architectural models by using Model-Driven Architecture and Software Product Line techniques. These models are automatically compiled and the object code (C#, in .NET) is generated obtaining an executable application.</p><p>In protoBOM, the user constructs Decision Support Systems in a simpler way by using the</p><p>ontologies of the diagnosis and the application domains by means of Domain Specific Languages. The interfaces will be closer to the problem domain, which will facilitate user interaction in a manner simple and intuitive.</p>

With new paradigms in software engineering such as Software Product Lines, scope of reuse is enlarged from implementation upto design, requirements, test-cases, etc. In this thesis an ontology-based approach is proposed as a solution to systematic requirement reuse problem in software product lines, and the approach is supported with a reuse automation tool. A case study is performed on the projects of an industrial software product line using hereby proposed solution and then based on the evaluated metrics it&rsquo / s reported that the content of requirements specifications documents can be prepared upto 80% by derivation of reusable requirements.

Reusability of assets within a family of products is the major goal of Software Product Line Engineering (SPLE), therefore managing variability is an important task in SPLs. Configuration management in the context of software product line engineering is more complicated than that in single systems engineering due to &rdquo / variability in space&rdquo / in addition to &rdquo / variability in time&rdquo / of core assets. In this study, a method for documenting variability in executable configuration items, namely components, is proposed by associating them with the Orthogonal Variability Model (OVM) which introduces variability as a separate model. The main aim is to trace variability in dierent configurations by explicitly documenting variability information for components. The links between OVM elements and components facilitate tool support for product derivation as the components matching the selected variations can be gathered by following the links. The proposed scheme is demonstrated on a case study about a radar GUI variability model.

A software product line (SPL) succeeds because we exploit the similarities between a set of software-intensive systems, together with an understanding of their differences, to reduce development cost, maintenance cost, and user confusion. In SPL engineering, reuse is planned, enabled, and enforced. It is through the development of a set of core assets that reuse is systematically practiced. Requirements assets enhance the effectiveness of reuse since engineers can work on the abstractions closer to the systems' initial concepts. Contemporary SPL requirements engineerin (RE) approaches often adopt the proactive model to build a relatively complete and stable asset base. In practice, the substantial up-front effort and the abrupt transition from existing practices associated with the proactive model present a prohibitive SPL adoption barrier for many organizations that could otherwise benefit. The extractive model overcomes these shortcomings by reusing existing products for the SPL's initial baseline. In this thesis, we present a framework for applying lightweight techniques to extract, model, and analyze a SPL's requirements assets. We define the notion of functional requirements profiles (FRPs) according to the linguistic characterization of a domain's action-oriented concerns, and show that the FRPs can be extracted from a natural language document on the basis of domain-aware lexical affinities that bear a 'verb - direct object' relation. We model the extracted FRPs by analyzing their semantic cases and by extending the orthogonal variability model (OVM). We contribute a set of heuristic rules for uncovering the variation dimensions and dependencies, and discuss merging the OVMs extracted from multiple sources. We relate functional profiles to quality requirements via scenarios, and manage requirements interactions via concept analysis. We present two applications of FRPs to support some other activities in SPL engineering. We conduct several empirical studies to evaluate our framework. The results show that our approach allows the engineers to identify the domain elements more easily and develop the domain models more systematically. Our work fills the void with respect to extracting a SPL's requirements assets, and the main thrust of our work is to promote a set of lightweight, low adoption threshold techniques as a critical enabler for practitioners to capitalize on the order-of-magnitude improvements offered by SPL engineering.

A software product line (SPL) consists of a set of software-intensive systems sharing a common, managed set of features that satisfy the specific needs of a particular market segment or mission and that are developed from a common set of core assets in a prescribed way. Together with testing of final deliverable products developed within the SPL, called Integration Testing, particularly important in this context is the way individual hardware as well as software components in an SPL are tested and certified for usage within the SPL. This study investigates specific approaches and techniques proposed in the literature for unit testing in the SPL context. Problems inherent to this issue were studied and possible solutions aiming towards systematic and effective testing of hardware as well as software units in SPLs have been proposed. The specific problems of SPL testing in ASELSAN were investigated in the light of these possible solutions and their applicability as well as their benefits were quantitatively assessed.

A software product line (SPL) succeeds because we exploit the similarities between a set of software-intensive systems, together with an understanding of their differences, to reduce development cost, maintenance cost, and user confusion. In SPL engineering, reuse is planned, enabled, and enforced. It is through the development of a set of core assets that reuse is systematically practiced. Requirements assets enhance the effectiveness of reuse since engineers can work on the abstractions closer to the systems' initial concepts. Contemporary SPL requirements engineerin (RE) approaches often adopt the proactive model to build a relatively complete and stable asset base. In practice, the substantial up-front effort and the abrupt transition from existing practices associated with the proactive model present a prohibitive SPL adoption barrier for many organizations that could otherwise benefit. The extractive model overcomes these shortcomings by reusing existing products for the SPL's initial baseline. In this thesis, we present a framework for applying lightweight techniques to extract, model, and analyze a SPL's requirements assets. We define the notion of functional requirements profiles (FRPs) according to the linguistic characterization of a domain's action-oriented concerns, and show that the FRPs can be extracted from a natural language document on the basis of domain-aware lexical affinities that bear a 'verb - direct object' relation. We model the extracted FRPs by analyzing their semantic cases and by extending the orthogonal variability model (OVM). We contribute a set of heuristic rules for uncovering the variation dimensions and dependencies, and discuss merging the OVMs extracted from multiple sources. We relate functional profiles to quality requirements via scenarios, and manage requirements interactions via concept analysis. We present two applications of FRPs to support some other activities in SPL engineering. We conduct several empirical studies to evaluate our framework. The results show that our approach allows the engineers to identify the domain elements more easily and develop the domain models more systematically. Our work fills the void with respect to extracting a SPL's requirements assets, and the main thrust of our work is to promote a set of lightweight, low adoption threshold techniques as a critical enabler for practitioners to capitalize on the order-of-magnitude improvements offered by SPL engineering.

In Software Product Lines (SPLs), a feature model can be used to represent the similarities and differences within a family of software systems. This allows describing the systems derived from the product line as a unique combination of the features in the model. What makes feature models particularly appealing is the fact that the constraints in the model prevent incompatible features from being part of the same product. Despite the benefits of feature models, constructing and maintaining these models can be a laborious task especially in product lines with a large number of features and constraints. As a result, the study of automated techniques to reason on feature models has become an important research topic in the SPL community in recent years. Two techniques, in particular, have significant appeal for researchers: SAT solvers and Binary Decision Diagrams (BDDs). Each technique has been applied successfully for over four decades now to tackle many practical combinatorial problems in various domains. Currently, several approaches have proposed the compilation of feature models to specific logic representations to enable the use of SAT solvers and BDDs. In this thesis, we argue that several critical issues related to the use of SAT solvers and BDDs have been consistently neglected. For instance, satisfiability is a well-known NP-complete problem which means that, in theory, a SAT solver might be unable to check the satisfiability of a feature model in a feasible amount of time. Similarly, it is widely known that the size of BDDs can become intractable for large models. At the same time, we currently do not know precisely whether these are real issues when feature models, especially large ones, are compiled to SAT and BDD representations. Therefore, in our research we provide a significant step forward in the state-of-the-art by examining deeply many relevant properties of the feature modeling domain and the mechanics of SAT solvers and BDDs and the sensitive issues related to these techniques when applied in that domain. Specifically, we provide more accurate explanations for the space and/or time (in)tractability of these techniques in the feature modeling domain, and enhance the algorithmic performance of these techniques for reasoning on feature models. The contributions of our work include the proposal of novel heuristics to reduce the size of BDDs compiled from feature models, several insights on the construction of efficient domain-specific reasoning algorithms for feature models, and empirical studies to evaluate the efficiency of SAT solvers in handling very large feature models.

In Software Product Lines (SPLs), a feature model can be used to represent the similarities and differences within a family of software systems. This allows describing the systems derived from the product line as a unique combination of the features in the model. What makes feature models particularly appealing is the fact that the constraints in the model prevent incompatible features from being part of the same product. Despite the benefits of feature models, constructing and maintaining these models can be a laborious task especially in product lines with a large number of features and constraints. As a result, the study of automated techniques to reason on feature models has become an important research topic in the SPL community in recent years. Two techniques, in particular, have significant appeal for researchers: SAT solvers and Binary Decision Diagrams (BDDs). Each technique has been applied successfully for over four decades now to tackle many practical combinatorial problems in various domains. Currently, several approaches have proposed the compilation of feature models to specific logic representations to enable the use of SAT solvers and BDDs. In this thesis, we argue that several critical issues related to the use of SAT solvers and BDDs have been consistently neglected. For instance, satisfiability is a well-known NP-complete problem which means that, in theory, a SAT solver might be unable to check the satisfiability of a feature model in a feasible amount of time. Similarly, it is widely known that the size of BDDs can become intractable for large models. At the same time, we currently do not know precisely whether these are real issues when feature models, especially large ones, are compiled to SAT and BDD representations. Therefore, in our research we provide a significant step forward in the state-of-the-art by examining deeply many relevant properties of the feature modeling domain and the mechanics of SAT solvers and BDDs and the sensitive issues related to these techniques when applied in that domain. Specifically, we provide more accurate explanations for the space and/or time (in)tractability of these techniques in the feature modeling domain, and enhance the algorithmic performance of these techniques for reasoning on feature models. The contributions of our work include the proposal of novel heuristics to reduce the size of BDDs compiled from feature models, several insights on the construction of efficient domain-specific reasoning algorithms for feature models, and empirical studies to evaluate the efficiency of SAT solvers in handling very large feature models.

<p>This report presents the results of a thesis project that surveys and designs about the issue “Feature Model for Adaptive Computing”. In this project, there are two main issues, first one is about the Feature Modeling, and the second is how to use this Feature Modeling for adaptive computing.</p><p>In this thesis report, at the beginning, we present the problem we expected to solve and introduce some background information, including the knowledge of feature model and adaptive computing. Then we explain our solution and evaluate this solution. At the end of this report, we give a short conclusion about our thesis project and feature work.</p>