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In the twenty-first century People with Learning Difficulties (PWLD) still face oppression, discrimination and exclusion from the mainstream of social life. Over recent decades the policy of the United Kingdom’s (UK) government and activist organisations regarding people with learning difficulties has been on enabling inclusion, ensuring rights, providing choice and developing advocacy and independence. People with learning difficulties have been moved out of institutions with the intention to be included and respected as equal members of society. During the same decades that the government and activist organisations have been striving for the inclusion and equality of people with learning difficulties, the use of Information Technology (IT) has reached pervasive levels, to the degree that it is almost impossible for individuals to socially function successfully, unless they have access to it. Unfortunately, most IT is not designed to be usable and accessible to people with learning difficulties and this is a major barrier for their social inclusion. Participatory Design (PD) methodologies which emphasise end-user involvement in the software development process are widely considered the key to system usability and accessibility. However, most researchers and software developers believe that people with learning difficulties are not capable of participating in the process of development as a result of their disabilities. Others, report that they do not know how to work with this specific group of disabled end-users. This discriminatory behaviour is a major reason why IT remains inaccessible to people with learning difficulties. The study described in this thesis combined Evolutionary Prototyping, a software development methodology and Participatory Action Research (PAR), a social science methodology, in order to involve a cohort of four Health Trainers with learning difficulties in the development of a Web 2.0 based system. The aims of the study were to explore how people with learning difficulties could be involved in the development of a software system and if they could use a system developed with their participation. A further aim was to explore how software developers can approach the field of Learning Disability, the issues they will face and how those issues can be overcome. Qualitative data was gathered during fourteen Participatory Action Research meetings, in which the Health Trainers were involved in research, software development and system use. The data was analysed using Thematic Content Analysis facilitated by the use of the NVivo software package. The findings were validated by the participating Health Trainers. The findings suggest that during software development participation, the Health Trainers faced a number of challenges. However, the Health Trainers indicated the type of support they needed from the researcher in order to overcome them. The support required was easy to provide and the Health Trainers managed to engage in the software development process. The study conducted a system use evaluation to explore if the developed system was usable and accessible to the Health Trainers. The Health Trainers managed to complete all the system tasks posed to them during the evaluation. This suggests that the developed system was usable and accessible to the Health Trainers. Further evidence suggests that a number of factors affected the participation of the Health Trainers during development and during the use of the system. Finally, the study explored how the developed system was used over the long run, in a period of eighteen months. The findings suggest that system use over time was affected by factors other than the system’s accessibility and usability. Concluding, the findings suggest that with easy to provide support, the Health Trainers with learning difficulties could be involved in software development and they could use a system developed with their participation. It is hoped that the findings be used by policy makers and advocacy groups, to make a case towards convincing researchers and software developers to involve more people with learning difficulties in software development, thus making systems accessible to this community of end-users.

Design is fundamental to software development. Indeed, early lifecycle software engineering design is crucial and has significant impact of subsequent development activities. Inferior designs can result in deleterious down-stream consequences. Therefore improving the traceability, structural integrity and elegance of software design has significant potential for enhancing software development productivity. However, early lifecycle software design is a demanding and non-trivial task for software engineers to perform and current computational tool support for software engineers is limited. Thus to address this limitation, this thesis investigates the potential of interactive evolutionary search and complementary computational intelligence to enable the exploration and discovery of useful and interesting software designs relating to the design problem at hand. To enable evolutionary search and exploration of possible design solutions, a novel, discrete, object-based representation of both design problem and design solution is proposed. Associated genetic operators including self-adapting mutation are also proposed. Experiments show that this novel representation enables highly effective search and exploration of the software design solution space. Next, software agents are introduced to facilitate an interactive framework for natural collaborative designer / computer interaction. Empirical investigations reveal that colourful visualisation of software designs engages the designer. Furthermore, with enhanced generation of multiple candidate designs, opportunities for periods of designer reflection are presented thus enabling sudden design discovery. Design elegance is an important but complex factor in software design. Four novel quantitative elegance measures are proposed which enhance the interactive design experience by selecting elegant software designs for designer evaluation. Using designer elegance evaluation as reward, reward-based machine learning is exploited to steer a dynamic, multi-objective search according to designer elegance intentions. Designer interactivity is further enhanced by a dynamic, fitness-proportionate interactive interval, which judiciously varies the number of evolutionary generations between interactions to promote search and exploration and further reduce use fatigue. The integration of interactive, dynamic evolutionary search with software agents and reward-based learning is found to produce an engaging, compelling interactive experience for software designers, successfully enabling the search, exploration and discovery of fruitful, interesting and useful early lifecycle software designs.

The work described in this thesis falls under the umbrella of the Aurora project (Aurora 2000). Aurora is a long-term research project which, through diverse studies, investigates the potential enhancement of the everyday lives of children with autism through the use of robots, and other interactive systems, in playful contexts. Autism is a lifelong pervasive disability which affects social interaction and communication. Importantly for this thesis, children with autism exhibit a deficit in narrative comprehension which adversely impacts their social world. The research agenda addressed by this thesis was to develop an interactive software system which promotes an understanding of narrative structure (and thus the social world) while addressing the needs of individual children. The conceptual approach developed was to break down narrative into proto-narrative components and address these components individually through the introduction of simple game-like tasks, called t-stories, presented in a human-computer interaction context. The overarching hypothesis addressed was that it is possible to help children with autism to improve their narrative skills by addressing proto-narrative components independently. An interactive software system called TouchStory was developed to present t-stories to children with autism. Following knowledge of the characteristics and preferences of this group of learners TouchStory maintained strong analogies with the concrete, physical world. The design approach was to keep things simple, introducing features only if necessary to provide a focussed and enjoyable game. TouchStory uses a touch-sensitive screen as the interaction device as it affords immediate direct manipulation of the t-story components. Socially mediated methods of requirements elicitation and software evaluation (such as focus groups, thinking aloud protocols, or intergenerational design teams) are not appropriate for use with children with autism who are not socially oriented and, in the case of children with ‗lower functioning‘ autism, may have very few words or no productive language. Therefore a new strategy was developed to achieve an inclusive, child-centred design; this was to interleave prototype development with evaluation over several long-term trials. The trials were carried out in the participants‘ own school environments to provide an ecologically valid contextual enquiry. In the first trial 18 participants were each seen individually once. The second and third trials were extended studies of 12 and 20 school visits with 12 and 6 participants respectively; each participant was seen individually on each school visit, provided that the participant was at school on the day of the visit. Evaluation was carried out on the basis of video recordings of the sessions and software logs of the on-screen interactions. Individual learning needs were addressed by adapting the set of t-stories presented to the participant on the basis of success during recent sessions. No ordering of difficulty among the proto-narrative categories could be known a priori for any individual child, and may vary from child to child. Therefore the intention was to gradually, over multiple sessions, increase the proportion of t-stories from proto-narrative categories which the individual participant found challenging, while retaining sufficient scope for the expression of skills already mastered for the session to be enjoyable and rewarding. The adaptation of the software was achieved by introducing a simple adaptive formula, evaluating it over successive long terms trials, and increasing the complexity of the formula only where necessary. Results indicate that individual participants found the interactive presentation of the simple game-like tasks engaging, even after repeated exposures on as many as 20 occasions. The adaptive formula developed in this study did, for engaged participants, focus on the proto-narrative categories which the participant needed to practice but was likely to succeed; that is it did target an effective learning zone. While little evidence was seen of learning with respect to the fully developed narratives encountered in everyday life, results strongly suggest that some participants were actively engaged in self-directed, curiosity-driven activity that functioned as learning in that they were able to transfer knowledge about the appropriateness of particular responses to previously unseen t-stories. This thesis was driven by the needs of children with autism; contributions are made in a number of cognate areas. A conceptual contribution was made by the introduction of the proto-narrative concept which was shown to identify narrative deficits in children with autism and to form a basis for learning. A contribution was made to computational adaptation by the development of a novel adaptive formula which was shown to present a challenging experience while maintaining sufficient predictability and opportunities for the expression of skills already mastered to provide a comfortable experience for children with autism. A contribution was made to software development by showing that children with autism may be included in the design process through iterative development combined with long term trials. A contribution was made to assistive technology by demonstrating that simplicity together with evaluation over long term trials engages children with autism and is a route to inclusion. We cannot expect any magic fixes for children with autism, progress will be made by small steps; this thesis forms a small but significant contribution.

The Unified Modeling Language (UML) has become the standard language for the design of object-oriented software systems over the past decade. Even though there exist various tools which claim to support design with UML, their functionality is usually focused on drawing UML diagrams and generating code from the UML model. The task of choosing a suitable design which fulfils the requirements still has to be accomplished by the human designer alone. The aim of this thesis is to develop concepts for UML design tools which assist the modeller in improving the system design and requirements incrementally. For this approach a variant of formal games called exploration games is introduced as underlying technique. Exploration games can be defined on the basis of incomplete and imprecise UML models as they occur frequently in practice. The designer repeatedly plays an exploration game to detect flaws or incompleteness in the design and its specification, which are both incorporated in the game definition. At any time the game definition can be incremented by the modeller which allows him to react to the discoveries made during a play and experiment with new design solutions. Exploration games can be applied to UML in different variants. For each variant must be specified how the UML diagrams are used to set up the game and how the semantic variation points of UML should be interpreted. Furthermore some parts of the game definition may not be contained in the UML model and have to be provided separately. The emphasis of this thesis is on game variants which make use of UML diagrams for modelling system behaviour, especially state machines and activity diagrams. A prototypical implementation demonstrates how the concepts developed in this thesis can be put into practice. The tool supports the user in defining, playing and incrementing a game. Moreover it can compute winning strategies for the players and may act as opponent of the modeller. As example a game variant based on UML state machines has been implemented. The architecture that has been chosen for the tool leaves room for extension by additional game variants and alternative algorithms.

Unified Modeling Language (UML) class diagrams are widely used throughout software design lifecycle to model the Software Requirement Specifications in developing any software. In many cases these class diagrams are initially drawn, as well as subsequently revised using hand in a piece of paper, or a whiteboard. Although these hand-drawn class diagrams capture most of the specifications, they need a lot of revision and visual inspection by the software architects for verification of the captured requirements, of the system being modeled. Manually verifying the correctness and completeness of the class diagrams involves a lot of redundant work, and can raise issues due to human errors, for e.g., diagrams not drawn to scale, typeface issues, unclear handwriting, and even missing requirements etc. In this paper, we propose a state-of-the-art technique that pipelines the object detection, text extraction and replication phase to parse requirements incorporated within the user-drawn class diagrams. The parsed output from the proposed system, can be used to keyword-match the requirements in the Software Requirement Specification (SRS) document. We show that the proposed system can localize the UML classes and relationships in the diagram with 100% accuracy and can identify the localized objects of each type with maximum mean Average Precision of 0.8584. We also show that the text can be efficiently parsed from the diagrams with the character error rate of 0.3043.

Computer simulations are an indispensable tool for analyzing complex, multiscale systems in a wide range of scientific and engineering disciplines. In many such systems, the governing dynamics occur at the micro scale, whereas quantities of interest manifest themselves on orders of magnitude larger macro scale. To reduce the computational cost, the simulations are ideally performed directly on the macro and not the micro scale. However, finding an effective macro-scale model that reflects well its micro-scale counterpart remains a key challenge for Computational Science. In this thesis, we apply machine learning algorithms to automatically identify a system's macro-scale representation, effective macro-scale dynamics and the transfer of information between micro and macro scales. We present Adaptive Learning of Effective Dynamics (AdaLED), a framework for building online macro-scale surrogate models of complex multiscale systems based on autoencoders and ensembles of recurrent neural networks. The framework trains the surrogate on the fly and continuously monitors its prediction accuracy. Depending on the accuracy, it automatically switches the control of the simulation between the micro and macro scale dynamics to achieve a balance between speed-up and accuracy. Compared to other approaches, AdaLED enables for the first time the creation of adaptive surrogate models with online accuracy estimation that operate on the level of a time step. AdaLED models allow us to accelerate parts of dynamics that are simpler to learn while leaving the more complex ones to the micro-scale dynamics. The thesis further touches on the problem of coupling known micro and macro models and handling open boundary conditions for particle dynamics, used in particle-continuum coupling in multiscale fluid dynamics. We propose ideas for generalizing existing algorithms, limited to static cubical and spherical domains, to complex time-varying geometries. In particular, we show the generalization of the method for cross-boundary mass flow and discuss the challenges of generalizing the remaining boundary force method required to remove near-boundary density artifacts. The thesis also addresses the essential and often overlooked aspects of software design of coupled simulations based on multiple existing single-model solvers. We propose a novel software design that helps achieve portable, solver-agnostic implementations of the coupling model while minimizing the overhead of portability. The design presented in the thesis allows us to reuse the same coupling codes with multiple combinations of single-model solvers, thus increasing its usefulness and impact.

Homomorphic encryption allows computation on encrypted ciphertext so that decrypted plaintext produces a semantically useful computation. Since this comes at a tremendous computation cost for general-purpose computing, the synthesis of these approaches remains in the nascent stages of research and reaches practicability only on small data despite the dire need for privacy-preserving techniques in an age of rampant data surveillance. There is, therefore, a need for an accessible toolkit exposing the power of homomorphic encryption for data scientists to apply to machine learning problems. Privacy-preserving machine learning can have a tremendously positive societal benefit by allowing ethical biomedical research, building public trust about the security of sensitive data, and preventing potential abuses and fraud that result from data breaches in increasingly common hacking incidents. This thesis will attempt to contribute to this direction by building a software library that operates on homomorphically encrypted data but remains approachable to data scientists and simultaneously runs on at least some modest datasets and low-power devices, key limitations that so far have hampered uptake of homomorphic encryption to machine learning. Finally, it will seek to quantify the feasible bounds of data sizes and trade-offs of encryption, decryption, prediction, model training time and the performance penalty incurred from encryption.

Software quality is increasingly becoming a differentiator between software products. Thisresulted in the development of new and improved approaches to software development like object-orientation and the development of software metrics to better manage the process of software development. Analyzing a large collection of software projects that use object-oriented programming in terms of object-oriented design quality metrics that measure the size, complexity, performance and quality of software could give us a good idea of how object-oriented programming is used in practice. Analyzing software projects on the extreme ends of the distributions of these metrics could give us specific examples of coding practices which influence design qualities. This analysis can be used to inform machine learning models that estimate code quality based on design metrics.In this thesis, I generated a repository containing the latest version of 226,793 softwareprojects taken from the Maven Central Repository. I statically analyzed each of these projects and measured 18 class-level design quality metrics for 10,608,920 classes and 8 package-level design quality metrics for 2,107,577 packages. I analyze the outlier projects in terms of object-oriented design quality metrics and evaluate their suitability for inclusion in training sets for machine learning models.

Stochastic reachability is an important verification tool that provides probabilistic assurances of safety in a variety of contexts. In engineered systems, safety may be synonymous with the ability of the system to avoid "bad" constraints on the state space, that constitute collision, departure from a flight envelope, or other undesirable phenomenon. In biomedical systems, assurance of safety (such as the mandate to ``do no harm'') are considerably complicated by the fact that there is a lack of physics-driven models, extensive signal processing is needed to capture the underlying state of the system, and the stochasticity inherent to the system may be quite large. However, assurances of safety are no less paramount. Stochastic reachability analysis is a framework that is well suited to address safety problems in biomedical systems. We are particularly motivated by the problem of circadian regulation, which is not only critical for those with diagnosed sleep disorders, e.g. Delayed Sleep-Wake Phase disorder, but also for shift workers, people traveling across multiple time zones, and those in regulated lighting environments that do not account for circadian regulation, e.g. hospitals. The primary focus of this thesis is on the development software systems and algorithms that can address some of the most significant challenges associated with safety guarantees in biomedical systems. Specifically, I focus on linear time-varying dynamical systems (which are a common approximation of nonlinear dynamical systems) with unstructured, non-Gaussian disturbances. The theoretical methods I develop provide conservative assurances of safety, despite a potentially unbounded disturbance input, in a computable manner. I have implemented these methods in a new MATLAB toolbox for stochastic reachability, SReachTools, that I have jointly developed. This toolbox is open-source and has been repeatability verified. Implementation of such methods in a clinical environment requires rigorous development, design, and testing of software, as well as integration with methods for signal processing, fault handling, and error reporting. As part of a collaboration on smart lighting, I created a software implementation of a smart lighting testbed, which provides the necessary functionality to implement feedback-based circadian control, such as those provided by stochastic reachability algorithms. The testbed software was validated in a pilot experiment, in which the testbed successfully ran continuously for 5 days for each of 3 subjects. Estimates of circadian phase are necessary for control, hence signal processing techniques to infer phase from circadian-regulated biometrics are important. We developed a method to infer circadian phase from invasively gathered brain temperature data, in collaboration with UNM neurosurgeons in the UNM Neurotrauma intensive care unit. A similar technique would be necessary to someday fully close the loop in the smart lighting clinical testbed.

Abstract: The 2008 debut of Bitcoin marked the first large-scale implementation of blockchain technology, and its decentralized approach to monetary systems has since been abstracted to more generalized purposes like distributed computing. Platforms like Ethereum, which function as a global, decentralized computing and data storage system, promise to bring the cost of decentralized knowledge production in line with the efficiencies afforded by the centralized, integrated computing systems that currently dominate the knowledge economy. Blockchain technologies have been investigated for a wide range of information management purposes, but their exploration within the realm of library and information studies has largely been nascent. Though many applications within the field have been envisioned, few have been explored in depth. Among the many functions performed in the field of librarianship, the work of cataloguers—which has always been performed in a decentralized manner—represents an intriguing use case. A review of current shared-cataloguing practices reveals that catalogues have become largely-centralized, divorced from public participation, dominated by an ethos of efficiency at the cost of quality, and essentially unaltered since the shift from physical to electronic catalogue storage more than 40 years ago. The evolution of blockchain technologies, paired with an intentional approach to shared catalogues that is open for use, transparency, and public participation, is explored in a conceptual framework and design based on the Ethereum platform. A theoretical design scheme grounded in the affordances of Ethereum, shaped by the principles of open source software development, and guided by the best practices of existing social information production systems results in a proposal for Catagora: an open source, open-for-use, transparent and participatory shared-cataloguing platform that reverses the trend towards architectural and political centralization and promises novel catalogue features such as complete revision history and distributed collaboration on the content and quality of catalogue entries. Blockchain technology, alone, cannot disrupt shared cataloguing practices; such a shift involves the voluntary and eager participation of cataloguers and members of the public in order to sustain and grow the system. The Catagora design concept presented in this thesis incorporates accessibility, collaboration and reputational systems that are intended to foster open participation, but these alone cannot guarantee a thriving, shared-cataloguing alternative to existing systems. Further exploration, in the form of a live implementation, is warranted; and lessons from existing large-scale library technology projects suggest that a centrally-coordinated implementation, targeting key cataloguing partners and driven by a passionate project champion, may provide a more complete picture of the blockchain’s potential to support open, shared cataloguing for the benefit of information seekers.

Abstract: Software energy efficiency has become a concern for the scale of operations in data centers and for the availability of battery-driven mobile devices. Developers now consider energy efficiency as one of the performance metrics. Unfortunately, developers are not trained enough and do not know how to produce energy efficient software. They demand guidelines and tools for developing energy efficient software. To help energy-aware developers (focusing on Android application developers), we make the following contributions. 1) Energy-aware application (app) developers, first and foremost, need to estimate their apps' energy consumption. Without any estimation, they would not know if their effort for energy optimization is effective. Considering Android as an example, we first present a reproducible machine learning-based energy consumption model. Results suggest that with more and more apps in training, we can improve the estimation accuracy of machine learning-based models. However, adding and running a new app requires writing a manual test script for the app, which hinders building an ever improving energy model. To alleviate this problem, we show that automatic random test generation with test selection heuristics can be used. These automatically generated tests can exercise the apps, so we can collect different resource usage (independent variables) and energy consumption of the apps (dependent variable). 2) We then argue that developers also need energy optimization guidelines: what makes an app more energy efficient? Along this direction, we provide three different guidelines. First, we show that the new HTTP/2 protocol offers reduced energy consumption when compared to the old HTTP/1.1. Also, the handshaking mechanism for adding transport layer security causes extra energy drain in mobile clients. Energy-aware web app developers should consider deploying the HTTP/2 protocol for saving energy consumption. Second, we show that developers, if prudent, do not need to worry about the energy impact of software logging which is crucial for monitoring apps' health. They need to log less frequently by grouping small log messages together. Finally, we present a new design pattern that developers can adopt for making view updates more energy efficient. We also discuss the potential trade-offs (e.g., user experience, and software maintenance cost) that developers should be aware of before adopting a design choice.

Tools help us perform tasks. But they also change the way we think. In five studies, I explored the lasting effects — the cognitive residuals — of using two common forms of automated spelling assistance, spell-checking and autocorrect. These different spelling assistance systems aim to provide similar spelling outcomes, but users interact quite differently with each. By comparing differences in spelling skills after using these different types of spelling assistance, I assessed differences in the cognitive residuals that arise from direct interaction (spell-checking) and passive support (autocorrect). Results suggest that the passive support provided by autocorrect is just as good as for the development of spelling skill as the direct interaction offered by traditional forms of spell-checking. However, I also demonstrate a new type of direct-interaction spell-checking that outperforms traditional approaches in creating cognitive residuals for users that help improve their spelling skills.

Crowdsourcing is gradually becoming an accepted form of work across different disciplines. Not surprisingly, it has attracted the attention of the software engineering community as well. Previous work started exploring the feasibility of crowdsourcing for software design by conducting experiments in which workers from Amazon Mechanical Turk were asked to engage in a set of software design tasks. It was found that, when workers are exposed to examples of previous designs, they generate overall lower quality contributions. The intuition is that, since these experiments displayed all previous contributions as examples to workers, the presence of low quality examples may have negatively influenced workers.This thesis compares the designs produced in the previous experiments to designs obtained in a new experiment in which examples were evaluated against pre-defined quality criteria before being displayed to workers. Only examples that were of sufficient quality were shared with workers, with the hope of stimulating them to provide higher quality designs. We report results from an analysis in which we compare the designs from the current and previous experiments in terms of quantity, diversity of ideas, quality, completeness, perceived task difficulty, and how often workers borrow elements from examples. The major findings are twofold. First, workers who were exposed to sufficient quality examples produced better quality work as compared to workers exposed to all examples. Second, the quality of the designs they produced still did not reach the quality of the designs produced by workers who were not exposed to examples at all.

Microtask crowdsourcing is a form of crowdsourcing in which work is decomposed into a set of small, self-contained tasks, which each can typically be completed in a matter of minutes. The approach has been used to address a number of different problems, ranging from labeling images to planning travel. To date, however, little is known about the potential of microtask crowdsourcing in software engineering. This thesis explores microtask crowdsourcing as applied to software design work. We particularly conducted a large study with Amazon Mechanical Turk workers, who each provided one or more solution alternatives for a small, partial software design problem. We included two experimental conditions: (1) user interface design work versus internal code design work, and (2) workers operating independently versus workers being shown previous designs from other workers. We report on various results concerning solution diversity, solution quality, and perceived task difficulty, across the different experimental conditions. Our primary findings show that: (1) it is feasible for a crowd to generate a broad range of solution alternatives for a software design problem, (2) solutions alternatives range all over the quality spectrum, and (3) many workers perceived the task as difficult.

The introduction of mobile devices to the pockets and handbags of people living all over the world has made the practice of mobile computing nearly ubiquitous in modern society. iSeeMe is an Android application that empowers the user through the revelation of the vast amount of private data that mobile devices are capable of silently capturing in the background. iSeeMe strives to provide the user with a means to correlate this passively-collected information with data of personal importance to the user. This report looks into the development and implementation of the iSeeMe solution. It discusses design decisions, describes the iSeeMe architecture, and outlines the process of engineering the application. It also examines the role of personal data in modern society and explores the mobile application market to see where iSeeMe will fit in among similar applications. Finally, it analyzes the results of the development effort and identifies areas for future enhancement.

This paper explores how known machine learning techniques can be applied in unique ways to simplify software and therefore dramatically increase its usability. As software has increased in popularity, its complexity has increased in lockstep, to a point where it has become burdensome. By shifting the focus from the software to the user, great advances can be achieved by way of simplification. The example problem used in this report is well known: suggest local dining choices tailored to a specific person based on known habits and those of similar people. By analyzing past choices and applying likely probabilities, assumptions can be made to reduce user interaction, allowing the user to realize the benefits of the software faster and more frequently. This is accomplished with Java Servlets, Apache Mahout machine learning libraries, and various third party resources to gather dimensions on each recommendation.

This thesis suggests a software architecture for a video discussion board using the Online Language Environment (OLE) Board as a case study. A more suitable architecture will enable it to meet its goals. Goals for the new board are to support future developers and users. Developer-oriented goals are ease of readability, maintenance, and enhancement. User-oriented goals are ease of use, new or improved features such as data collection, and enhanced security. The old system is out of date, making planned expansion difficult. The new system uses a Model-View-Controller architecture utilizing updated frameworks. Updated protocols based on XML were introduced, and elements were added to allow for new features. Developer goals have been met with minimal drawbacks. The new board was used by 42 students at the University of Aizu and Rose-Hulman Institute of Technology. New features proved useful and security was increased, with some decrease in usability during initial testing.

I have created a program to assist in making characters for the Savage Worlds tabletop role-playing game published by Pinnacle Entertainment Group. This pro- gram was written in Java and uses a SQLite database to store character options. Principles of user-centered design were followed to make the program intuitive and easy to use and feedback from potential users was gathered. Programming design patterns were used to make it easier for me to implement and maintain the program. Ultimately, this program has been a culmination of my learning here at Wittenberg University and has been an opportunity for me to independently expand my knowledge of Computer Science.

A framework for fault-failure and error propagation through different UML diagrams is introduced. The method is formalized by defining rules for fault propagation across different UML diagrams and also within a particular diagram. A study of the propagation of faults through each diagram highlights various structural and behavioral aspects of the software failure. This method will allow the designers to proactively analyze the functionality of the systems early in the design process, understand functional and other failures and their propagation paths, overall impact on the system, and redundancies and safeguards that should be added. The main advantage of the method is that it permits the analysis of the failure and fault propagation at a highly abstract level before any potentially high-cost design commitments are made thereby supporting decision making early in the design process, providing guidance to the designers to allow elimination of failures through exploration of system components and their functionality, and facilitating the development of more reliable system configurations. This method is discussed using an example and results are given for the Helium tank sub-system of the Space Shuttle's Reaction Control System (RCS). The work is further extended to automate the FPSA and build an executable model of FPSA

This Capstone Project will address two important problems. The first problem concerns resolving priority conflicts over the projects among the managers in an organization and the second problem concerns the gap between the projects and the organizational strategies. To complete this project, I have developed an online project and portfolio management application by using the software development life cycle methodology and project management methodology. The outcome of this project is a software product that helps in obtaining a general consensus as to which project has the highest priority. This priority awareness helps in resolving conflicts among the managers. This product will force organizations to use the consistent computerized model, the model that helps the organizations to select and prioritize the projects based on their goals and strategic plans.

Software systems today face many challenges that were not even imagined decades prior. Challenges including the need to evolve at a very high rate, lifecycle phase drift or erosion, inability to prevent the butterfly effect where the slightest change causes unimaginable side effects throughout the system, lack of discipline to define metrics and use measurement to drive operations, and no "silver bullet" or single solution to solve all the problems of every domain, just to name a few. This is not to say that the issues stated above are the only problems. In fact, it would be impossible to list all possible problems--software itself is infinitely flexible bounded only by the human imagination. These are just a portion of the primary challenges today's software engineer faces. There have been attempts throughout the history of software to resolve each one of these challenges. There have been those who tried to tackle them individually, simultaneously, as well as various combinations of them at one time. One such method was to define and encapsulate the various phases within software, which has come to be called a software lifecycle or lifecycle model. Another area of recent research has lead to the hypothesis that many of these challenges can be resolved or at least facilitated through proper traceability methods. Virtually none of today's software components are completely derived from scratch. Rather, code reuse and software evolution become a large portion of the software engineer's duties. As Vance Hilderman at HighRely puts it, "Research has shown that proper traceability is vital. For high quality and safety-critical engineering development efforts however, traceability is a cornerstone not just for achieving success, but to proving it as well." So if software is not derived from scratch, having the traceability to know about its origination is invaluable. Given today's struggles, what is in store for the future software engineer? This paper is an attempt to quantify and answer (or at least project a possibility) that involves a new mindset and a new lifecycle model or structure change that may assist in tackling some of the above referenced issues.

Engineers in diverse fields are able to model their design and experiment with that design to determine how it responds to the environment and how it satisfies the requirements. Design tools for software engineering have become standardized and matured to allow for formal definition of software design. This paper tests the current state of design documentation to determine the quality of design testing available at the early stage of software design.

Formal specification languages can be used to support the rigorous development of complex software systems when these systems must be of high quality. Unfortunately, writing formal specifications and refining them into designs can be a challenging activity. Use of design patterns, which are a widely accepted design activity, helps create quality designs, but adds further complexity to the design activity.We have developed a tool ADEPT, Advanced Design Employing Pattern Templates, that aids designers in using both formal specifications and design patterns. The software developer will use the ADEPT tool to guide them through the process of choosing a design pattern that is related to their formal system specification for the purpose of automatically supporting refinement. The user is guided through refining the specification and creating a design that not only incorporates one of the design patterns but also meets the given system specification.

Genetic epidemiology relies on simulated data to support development of theory and methods. Simulated data are designed to reflect, as accurately as possible, the true phenotypic and genotypic distributions of individuals sampled from differenttypes of relationship clusters. Common simulation methods can be classified into two groups: agent-based methods, in which individuals are simulated, one at a time, according to the rules of Mendelian inheritance and other assumptions, and structure-based methods, in which aggregates of individuals exhibiting properties of interest are simulated, with genotypic information inferred conditionally on both phenotype and structure. A previously untried software design is proposed to support both agent-based and structure-based simulation with equal facility. The mediator design pattern is applied to simulation design, and is shown to (1) reduce thecomplexities arising from the potentially huge number of communication channels required between autonomous agents and (2) to provide an efficient mechanism by which higher-level system objects may override default behaviors of lower-levelobjects.

This thesis is about early detection of requirements defects. Software-centred systems’ defects can cause loss of life, loss of property, loss of data and economic losses. Requirements defects are a major source of system defects. The early detection of requirements defects prevents software-centred systems’ defects, and thus reduces the various types of losses. In the past thirty years, many methods have been developed to detect requirements defects. The most prominent methods include inspections, automated static analysis, simulation, formal specifications and more recently model-checking. Each method has different strengths and weaknesses. The lack of integration of the different detection techniques produces a knowledge gap that causes problems with repeatability, scalability, effectiveness, and efficiency of the detection process. This knowledge gap is enlarged by the lack of a well-specified defect classification scheme that specifies quality rules, collects defects, specifies defect patterns, and classifies the patterns. This thesis proposes a framework for early defect detection based on Behavior trees, a representation which makes it practical to integrate the various detection techniques. Individual requirements are translated one at a time into Requirements Behavior Trees. These Requirements Behavior Trees are then integrated into an Integrated Behavior Tree that can be inspected, statically analysed, model checked and simulated. The framework is based on the hypothesis that if a well-specified defect classification scheme is developed and different types of detectors are integrated to detect patterns that suit their capabilities and if processes are developed to cover the complete requirements lifecycle, then the framework’s detection results will be more effective and more efficient, and the results will be more repeatable and scalable than existing methods. The framework includes a Behavior Trees defect classification scheme. The scheme defines defect patterns for requirements written in English and requirements specified by Behavior Trees. The scheme has a variety of defect patterns. Each defect pattern contains the characteristics of a type of defect. Defect patterns are grouped together based on the quality rules that they violate. This framework and the hypothesis have been tested using four case studies. The results of the case studies found that compared to the Perspective-based Reading method and three conventional requirements analysis methods the framework proposed was more effective and able to detect a broader range of defect types. However, because of the lack of the tool support, the efficiency of the method is still questionable. It should however improve with better tool support.

Software design is an art. More often than not, designers rely on craftsmanship and creativity for designing software products. Just as understanding of the structure of atomic elements is critical to the natural sciences, it too is fundamental to the science behind software engineering. Structural theories simplify our understanding of a convoluted mechanism and allow us to reason in a clear and concise manner. Therefore, decision making can transcend subjective justification and move to quantitative and precise reasoning. Feature-Oriented Programming and compositional programming embody ideas of software decomposition structure. Feature-Oriented Programming treats Features as the building-blocks that are composed to synthesize programs. Its convergence with algebraic transformations has led to AHEAD - a simple algebraic model of Feature-Oriented Programming. Within AHEAD, reasoning over simple algebraic structures enables codification and mechanization of well-understood principles. In this thesis we demonstrate just that by showing that critical properties of a decomposition can be identified and verified efficiently. First, we introduce multidimensional structures, a higher-order structural relationship within conventional decomposition. Multidimensional structures simplify a complex decomposition’s representation, understanding, and compositional specification. However, not all decompositions are fit for a multidimensional arrangement. We identify an essential property of orthogonality that a decomposition and its implementation must satisfy in order to warrant a multidimensional structure. Next, we identify a class of errors that occur at module composition-time. These composition errors are a result of unsatisfied dependencies when feature modules are composed in arbitrary arrangements. AHEAD introduces architectural-level domain constraints that govern the compatibility of feature modules. Besides architectural-level composition constraints, there are also low-level implementation constraints: a feature module can reference classes that are defined in other feature modules. Safe composition is the guarantee that programs composed from feature modules are absent of references to undefined classes, methods, and variables. We show how safe composition can be guaranteed for AHEAD product lines using feature models and SAT solvers. The significance of this work lies in the generality of our approach. Multidimensional structures are not unique to AHEAD; we demonstrate its applicability under any form of decomposition. Likewise, safe composition also extends AHEAD. We draw an analogy between safe composition and C++ linking errors. Moreover, we demonstrate how non-code artifacts benefit from modularization as well as safe composition analysis. In identifying and verifying key properties, we have also demonstrated how the structural model of AHEAD can simplify our understanding and applicability of essential principles. When the fundamental structure is well-understood, software design is amenable to automation.

Much computer communications activity is invisible to the user, happening without explicit permission. When system administrators investigate network communications activities, they have difficulty tracing them back to the processes that cause them. The strictly layered TCP/IP networking model that underlies all widely used, general-purpose operating systems makes it impossible to trace a packet seen on the network back to the processes that are responsible for generating and receiving it. The TCP/IP model separates the concerns of network routing and process ownership so that the layers cannot share the information needed to correlate packets to processes. But knowing what processes are responsible for communications activities can be a great help in determining whether that activity is benign or malicious. My solution combines a visualization tool, a kernel-level correlation engine, and middleware that ties the two together. My research enables security personnel to visually correlate packets to the processes they belong to helping users determine whether communications are benign or malicious. I present my discoveries about the system administrator community and relate how I created a new correlation technology. I conducted a series of initial interviews with system administrators to clarify the problem, researched available solutions in the literature, identified what was missing, and worked with users to build it. The users were my co-designers as I built a series of prototypes of increasing fidelity and conducted usability evaluations on them. I hope that my work will demonstrate how well the participatory design approach works. My work has implications for the kernel structure of all operating system kernels with a TCP/IP protocol stack and network model. In light of my research, I hope security personnel will more clearly see sets of communicating processes on a network as basic computational units rather than the individual host computers. If kernel designers incorporate my findings into their work, it will enable much better security monitoring than is possible today making the Internet safer for all.

Practical full body tracking remains a difficult challenge related to the construction of immersive synthetic environment systems. Human body posture can be tracked in real-time using small, inexpensive inertial/magnetic sensor modules to track the orientation of individual limb segments and using the individual orientations to determine body posture. If the position of one point on the body is tracked, the position of all body segments can be determined. The software required for such a system is inherently complex due to its real-time distributed nature and the necessity to interface with varying sensor types and numbers. Furthermore, the software must be modular and flexible to ease enhancements and modifications. This thesis describes the implementation and design of a software system capable of processing data from a minimum of fifteen inertial/magnetic sensors at an update rate in excess of 100 Hz. Sensor data can be collected by a wearable computer and submitted via wireless LAN to a fixed workstation. The workstation processes data and acts as a server for avatar animation data. Design of the system software was completed using object-oriented techniques and the unified modeling language (UML). Several conversion classes process raw data from the fifteen sensors before being processed by a quaternion based complementary filtering algorithm to produce orientation estimates in quaternion form. Estimates are sent to a graphical display unit through a local area network. At any point through the conversion process, data may be archived for data analysis purposes or reprocessing.