Geographical Information Systems and geospatial data are seeing widespread use in various internet and mobile mapping applications. One of the areas where such technologies can be particularly valuable is aeronautical navigation. Pilots use paper charts for navigation, which, in contrast to modern mapping software, have some limitations. This project aims to develop an iOS application for phones and tablets that uses a GeoPackage database containing aeronautical geospatial data, which is rendered on a map to create an offline, feature-based mapping software to be used for navigation. Map features are selected from the database using R-Tree spatial indices. The attributes from each feature within the requested bounds are evaluated to determine the styling for that feature. Each feature, after applying the aforementioned styling, is drawn to an interactive map that supports basic zooming and panning functionalities. The application is written in Swift 3.0 and all features are drawn using iOS Core Graphics

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Software Product Lines (SPLs) are a successful approach to software reuse in the large. Even though tools exist to create SPLs, their evolution is widely unexplored. Evolving an SPL manually is tedious and error-prone as it is hard to avoid unintended side-effects that may harm the consistency of the SPL. In this thesis, the conceptual basis of a system for the evolution of model-based SPLs is presented, which maintains consistency of models and feature mapping. As basis, a novel classification is introduced that distinguishes evolutions by their potential to harm the mapping of an SPL. Furthermore, multiple remapping operators are presented that can remedy the negative side-effects of an evolution. A set of evolutions is complemented with appropriate remapping operations for the use in SPLs. Finally, an implementation of the evolution system in the SPL tool FeatureMapper is provided to demonstrate the capabilities of the presented approach when co-evolving models and feature mapping of an SPL.

Software Product Lines (SPLs) are a successful approach to software reuse in the large. Even though tools exist to create SPLs, their evolution is widely unexplored. Evolving an SPL manually is tedious and error-prone as it is hard to avoid unintended side-effects that may harm the consistency of the SPL. In this thesis, the conceptual basis of a system for the evolution of model-based SPLs is presented, which maintains consistency of models and feature mapping. As basis, a novel classification is introduced that distinguishes evolutions by their potential to harm the mapping of an SPL. Furthermore, multiple remapping operators are presented that can remedy the negative side-effects of an evolution. A set of evolutions is complemented with appropriate remapping operations for the use in SPLs. Finally, an implementation of the evolution system in the SPL tool FeatureMapper is provided to demonstrate the capabilities of the presented approach when co-evolving models and feature mapping of an SPL.:1 Introduction 1.1 Motivation for Evolving Software Product Lines 1.2 Outline of the Thesis 2 Background and Scope 2.1 Concepts and Terminology 2.1.1 Software Product Lines 2.1.2 Model-Driven Software Development 2.1.3 FeatureMapper 2.2 Scope 2.3 Related Work 3 Evolution of Software Product Lines 3.1 Evolutions 3.1.1 Evolutions in the Problem Space 3.1.2 Evolutions in the Solution Space 3.2 Classification Systems for Evolutions 3.2.1 Classification by Behavior Preservation 3.2.2 Classification Systems in the Literature 3.2.3 Classification by Semantical Extent of Model Changes 3.3 Remapping Operations 3.3.1 Remapping in the Problem Space 3.3.2 Remapping in the Solution Space 3.4 Classification and Remapping of Evolutions 3.4.1 Classification and Remapping of Problem Space Evolutions 3.4.2 Classification and Remapping of Solution Space Evolutions 4 A Framework for Evolutions in FeatureMapper 4.1 Relevant Technology 4.1.1 Refactory 4.1.2 EMFText 4.2 Implementation 4.2.1 Implementation of the Evolutions System 4.2.2 Implementation of the Remapping System 4.2.3 Implementation of the User Interface System 4.2.4 Implementation of the Test Suite 4.3 Possibilities for Extension 4.3.1 Adding New Evolutions 4.3.2 Adapting Existing Evolutions 5 Example Project 5.1 Initial Situation in 2001 5.2 First Revision in 2006 5.2.1 Removing the Cassette Player 5.2.2 Adding an MP3 CD Player 5.2.3 Adding a Personal Navigation Device 5.2.4 Changing the Implementation of the UI Builder 5.2.5 Summary of the Changes of the First Revision in 2006 5.3 Second Revision in 2011 5.3.1 Creating a Multi-Format CD Player 5.3.2 Enhancing Voice Recognition to Control the Audio Player 5.3.3 Restructuring Personal Navigation Maps 5.3.4 Changing the Implementation of the CD Player 5.3.5 Summary of the Changes of the Second Revision in 2011 5.4 Conclusion of the Example Project 6 Conclusion 6.1 Summarized Findings 6.2 Limitations and Drawbacks 6.3 Possibilities for Future Work 6.4 Theoretical and Practical Contributions A Object Remapping Specification (*.orspec) A.1 Object Remapping Specification Model A.2 Object Remapping Specification Syntax B DocBooklet (*.docbooklet) B.1 DocBooklet Model B.2 DocBooklet Syntax C NavMap (*.navmap) C.1 NavMap Model C.2 NavMap Syntax List of Figures List of Tables List of Listings Bibliography

Design space exploration (DSE) is a tool used to evaluate and compare designs as part of the design selection process. While evaluating every possible design in a design space is infeasible, understanding design behavior and response throughout the design space may be accomplished by evaluating a subset of designs and interpolating between them using surrogate models. Surrogate modeling is a technique that uses low-cost calculations to approximate the outcome of more computationally expensive calculations or analyses, such as finite element analysis (FEA). While surrogates make quick predictions, accuracy is not guaranteed and must be considered. This research addressed the need to improve the accuracy of surrogate predictions in order to improve DSE of structural responses. This was accomplished by performing comparative analyses of linear and nonlinear dimensionality-reduction-based radial basis function (RBF) surrogate models for emulating various FEA nodal results. A total of four dimensionality reduction methods were investigated, namely principal component analysis (PCA), kernel principal component analysis (KPCA), isometric feature mapping (ISOMAP), and locally linear embedding (LLE). These methods were used in conjunction with surrogate modeling to predict nodal stresses and coordinates of a compressor blade. The research showed that using an ISOMAP-based dual-RBF surrogate model for predicting nodal stresses decreased the estimated mean error of the surrogate by 35.7% compared to PCA. Using nonlinear dimensionality-reduction-based surrogates did not reduce surrogate error for predicting nodal coordinates. A new metric, the manifold distance ratio (MDR), was introduced to measure the nonlinearity of the data manifolds. When applied to the stress and coordinate data, the stress space was found to be more nonlinear than the coordinate space for this application. The upfront training cost of the nonlinear dimensionality-reduction-based surrogates was larger than that of their linear counterparts but small enough to remain feasible. After training, all the dual-RBF surrogates were capable of making real-time predictions. This same process was repeated for a separate application involving the nodal displacements of mode shapes obtained from a FEA modal analysis. The modal assurance criterion (MAC) calculation was used to compare the predicted mode shapes, as well as their corresponding true mode shapes obtained from FEA, to a set of reference modes. The research showed that two nonlinear techniques, namely LLE and KPCA, resulted in lower surrogate error in the more complex design spaces. Using a RBF kernel, KPCA achieved the largest average reduction in error of 13.57%. The results also showed that surrogate error was greatly affected by mode shape reversal. Four different approaches of identifying reversed mode shapes were explored, all of which resulted in varying amounts of surrogate error. Together, the methods explored in this research were shown to decrease surrogate error when performing DSE of a turbomachine compressor blade. As surrogate accuracy increases, so does the ability to correctly make engineering decisions and judgements throughout the design process. Ultimately, this will help engineers design better turbomachines.