Transformations play a central role in Model Based Software Engineering. Similar to the development of other types of software, a transformation's specification and implementation does not necessarily remain static over the course of a project's lifetime; the transformation may develop incrementally and evolve. The goal of this thesis is to propose metrics that can be used to characterize the evolution of model transformations. To perform an initial demonstration of the metrics, this thesis considers an incrementally defined model transformation task. The transformation is implemented using two model transformation languages, a textual language and a graphical language, and metrics are extracted from the historical artifacts. The thesis defines a set of change metrics based on an abstract syntax difference model. Language feature metrics are also defined for both transformation languages. A process for extracting model-based change metrics and language metrics from the abstract syntax of the transformation languages is introduced. The applicability of the metrics in characterizing changes is demonstrated using exploratory clustering analysis on a transformation task. We show how, for this transformation task using both languages, metrics derived from the difference model result in clusters that reflect characteristics of individual changes, in contrast to clusters obtained with language metrics. / Thesis (Master, Computing) -- Queen's University, 2012-09-28 10:47:10.999

Rich Internet Applications (RIAs) are a new generation of web applications that break away from the concepts on which traditional web applications are based. RIAs are more interactive and responsive than traditional web applications since RIAs allow client-side scripting (such as JavaScript) and asynchronous communication with the server (using AJAX). Although these are improvements in terms of user-friendliness, there is a big impact on our ability to automatically explore (crawl) these applications. Traditional crawling algorithms are not sufficient for crawling RIAs. We should be able to crawl RIAs in order to be able to search their content and build their models for various purposes such as reverse-engineering, detecting security vulnerabilities, assessing usability, and applying model-based testing techniques. One important problem is designing efficient crawling strategies for RIAs. It seems possible to design crawling strategies more efficient than the standard crawling strategies, the Breadth-First and the Depth-First. In this thesis, we explore the possibilities of designing efficient crawling strategies. We use a general approach that we called Model-based Crawling and present two crawling strategies that are designed using this approach. We show by experimental results that model-based crawling strategies are more efficient than the standard strategies.

The subject of this thesis is the design and practical application of a model-based controller with variable structure control (VSC). Robot manipulators are highly non-linear systems, however they form a specific class in the non-linear group. Exact mathematical descriptions of the robot dynamics can be achieved and further, robot manipulators have specific useful properties that can be used for the design of advanced controllers. The inclusion of the inverse dynamic description of the robot manipulator as a feedforward term of the controller (model-based controller) is used to transform two non-linear systems i.e. the controller and the robot, into one linear system. The limitation of this technique arises from the accuracy of the inverse dynamic model. The linearisation only takes place if the model is known exactly. To deal with the uncertainties that arise in the model, a control methodology based on variable structure control is proposed. The design of the controller is based on a Lyapunov approach and engineering considerations of the robot. A candidate Lyapunov function of a pseudo-energy form is selected to start the controller design. The general form of the controller is selected to satisfy the negative definiteness of the Lyapunov function. The initial uncertainties between the actual robot dynamics and the model used in the controller are dealt with using a classical VSC regulator. The deficiencies of this approach are evident however because of the chattering phenomenum. The model uncertainties are examined from an engineering point of view and adjustable bounds are then devised for the VSC regulator, and simulations confirm a reduction in the chattering. Implementation on the SPRINTA robot reveals further limitations in the proposed methodology and the bound adjustment is enhanced to take into account the position of the robot and the tracking errors. Two controllers based on the same principle are then obtained and their performances are compared to a PID controller, for three types of trajectory. Tests reveal the superiority of the devised control methodology over the classic PID controller. The devised controller demonstrates that the inclusion of the robot dynamics and properties in the controller design with adequate engineering considerations lead to improved robot responses.

A measurement-based reduced-order heat transfer modeling framework is developed to optimize cooling costs of dynamic and virtualized data centers. The reduced-order model is based on a proper orthogonal decomposition-based model order reduction technique. For data center heat transfer modeling, the framework simulates air temperatures and CPU temperatures as a parametric response surface with different cooling infrastructure design variables as the input parameters. The parametric framework enables an efficient design optimization tool and is used to solve several important problems related to energy-efficient thermal design of data centers. The first of these problems is about determining optimal response time during emergencies such as power outages in data centers. To solve this problem, transient air temperatures are modeled with time as a parameter. This parametric prediction framework is useful as a near-real-time thermal prognostic tool. The second problem pertains to reducing temperature monitoring cost in data centers. To solve this problem, transient air temperatures are modeled with spatial location as the parameter. This parametric model improves spatial resolution of measured temperature data and thereby reduces sensor requisition for transient temperature monitoring in data centers. The third problem is related to determining optimal cooling set points in response to dynamically-evolving heat loads in a data center. To solve this problem, transient air temperatures are modeled with heat load and time as the parameters. This modeling framework is particularly suitable for life-cycle design of data center cooling infrastructure. The last problem is related to determining optimal cooling set points in response to dynamically-evolving computing workload in a virtualized data center. To solve this problem, transient CPU temperatures under a given computing load profile are modeled with cooling resource set-points as the parameters.

Defects in computer programs have great social and economic impacts and should be eliminated as much as possible. Since testing and debugging are among the most costly and time consuming tasks in the software development life cycle, a variety of intelligent debugging aids have been proposed within the last three decades. Model-based software debugging (MBSD) is a particular technique that exploits discrepancies between a program execution and the intended behaviour to isolate program fragments that could potentially explain an observed misbehaviour. In contrast to other techniques, model-based debugging does not require a formal specification of a program's behaviour, making the approach suitable for developers without training in formal software engineering practices. A key aspect of model-based debugging is the transformation of the given program into a model suitable for debugging. In this thesis, several models for analysing programs written in an object-oriented language are investigated, with Java as concrete example. The aim of this work is to assess the suitability of value-based models and generalisations thereof for debugging of programs making use of dynamically allocated data structures, recursive methods and polymorphic method invocations.

Agile autonomous systems are emerging, such as unmanned aerial vehicles (UAVs), that must robustly perform tightly coordinated time-critical missions; for example, military surveillance or search-and-rescue scenarios. In the space domain, execution of temporally flexible plans has provided an enabler for achieving the desired coordination and robustness, in the context of space probes and planetary rovers, modeled as discrete systems. We address the challenge of extending plan execution to systems with continuous dynamics, such as air vehicles and robot manipulators, and that are controlled indirectly through the setting of continuous state variables.Systems with continuous dynamics are more challenging than discrete systems, because they require continuous, low-level control, and cannot be controlled by issuing simple sequences of discrete commands. Hence, manually controlling these systems (or plants) at a low level can become very costly, in terms of the number of human operators necessary to operate the plant. For example, in the case of a fleet of UAVs performing a search-and-rescue scenario, the traditional approach to controlling the UAVs involves providing series of close waypoints for each aircraft, which incurs a high workload for the human operators, when the fleet consists of a large number of vehicles.Our solution is a novel, model-based executive, called Sulu, that takes as input a qualitative state plan, specifying the desired evolution of the state of the system. This approach elevates the interaction between the human operator and the plant, to a more abstract level where the operator is able to “coach” the plant by qualitatively specifying the tasks, or activities, the plant must perform. These activities are described in a qualitative manner, because they specify regions in the plant’s state space in which the plant must be at a certain point in time. Time constraints are also described qualitatively, in the form of flexible temporal constraints between activities in the state plan. The design of low-level control inputs in order to meet this abstract goal specification is then delegated to the autonomous controller, hence decreasing the workload per human operator. This approach also provides robustness to the executive, by giving it room to adapt to disturbances and unforeseen events, while satisfying the qualitative constraints on the plant state, specified in the qualitative state plan.Sulu reasons on a model of the plant in order to dynamically generate near-optimal control sequences to fulfill the qualitative state plan. To achieve optimality and safety, Sulu plans into the future, framing the problem as a disjunctive linear programming problem. To achieve robustness to disturbances and maintain tractability, planning is folded within a receding horizon, continuous planning and execution framework. The key to performance is a problem reduction method based on constraint pruning. We benchmark performance using multi-UAV firefighting scenarios on a real-time, hardware-in-the-loop testbed. / SM thesis

Submitted by Dilene Paulo (dilene.fatima@ufcg.edu.br) on 2018-01-25T12:53:52Z No. of bitstreams: 1 ADRIANA CARLA DAMASCENO – TESE PPGCC 2015.pdf: 3272500 bytes, checksum: 779024871de692299535f2de0eeabfb9 (MD5) / Made available in DSpace on 2018-01-25T12:53:52Z (GMT). No. of bitstreams: 1 ADRIANA CARLA DAMASCENO – TESE PPGCC 2015.pdf: 3272500 bytes, checksum: 779024871de692299535f2de0eeabfb9 (MD5) Previous issue date: 2015-03-06 / We can find many examples of Real-time Systems (RTS) in critical applications such as patient monitoring, air traffic control and others. A failure in this kind of system can be catastrophic. For example, it can harm human lives or increase project budgets. Hence, the testing of real-time systems must be accurate. Models are used to perform this task, since they contain information about how the system behaves and when actions may happen. Due to the complexity of the available systems, most RTS are composed of subsystems that interact as part of a bigger system. These subsystems are combined through operators to model their specification behavior. However, works on the testing of compositional models for RTS are practically nonexistent. Among the available approaches to perform testing for non-compositional RTS models, the tioco conformance testing theory focuses on generating test cases based on implementation and specification models. Moreover, a conformance relation defines whether success in testing means conformance between an implementation and a specification. To express specifications and to represent implementations under test, we use Timed Input Output Symbolic Transitions Systems (TIOSTS). These models store symbolic data and clock variables, avoiding the state space and region explosion problems. Regarding the testing of compositional models, some questions may arise: If two subsystem implementations are tioco conformant to their specifications, is it correct to assume that the composition of the implementations is also tioco conformant to the composition of their specifications? In this case, how can operators be defined to work with TIOSTS and tioco? To answer these questions, this thesis proposes the sequential, interruption and parallel operators for the TIOSTS model. For each operator, we study how the tioco conformance relation behaves with respect to subsystems and the composed system. We present results towards properties of compositional operators when the subsystems are composed, as well as implementing them. Besides, we show three examples where each operator can be used and illustrate the applicability of our approach in two exploratory studies. The first models components of a aircraft specification and the second presents application level interruptions in an Android system. / We can nd many examples of Real-time Systems (RTS) in critical applications such as patient monitoring, air tra c control and others. A failure in this kind of system can be catastrophic. For example, it can harm human lives or increase project budgets. Hence, the testing of real-time systems must be accurate. Models are used to perform this task, since they contain information about how the system behaves and when actions may happen. Due to the complexity of the available systems, most RTS are composed of subsystems that interact as part of a bigger system. These subsystems are combined through operators to model their speci cation behavior. However, works on the testing of compositional models for RTS are practically nonexistent. Among the available approaches to perform testing for non-compositional RTS models, the tioco conformance testing theory focuses on generating test cases based on implementation and speci cation models. Moreover, a conformance relation de nes whether success in testing means conformance between an implementation and a speci cation. To express speci cations and to represent implementations under test, we use Timed Input Output Symbolic Transitions Systems (TIOSTS). These models store symbolic data and clock variables, avoiding the state space and region explosion problems. Regarding the testing of compositional models, some questions may arise: If two subsystem implementations are tioco conformant to their speci cations, is it correct to assume that the composition of the implementations is also tioco conformant to the composition of their speci cations? In this case, how can operators be de ned to work with TIOSTS and tioco? To answer these questions, this thesis proposes the sequential, interruption and parallel operators for the TIOSTS model. For each operator, we study how the tioco conformance relation behaves with respect to subsystems and the composed system. We present results towards properties of compositional operators when the subsystems are composed, as well as implementing them. Besides, we show three examples where each operator can be used and illustrate the applicability of our approach in two exploratory studies. The rst models components of a aircraft speci cation and the second presents application level interruptions in an Android system.

Generating code from software models is considered to be a new generation leap within software development methods. The objective of this M.Sc. project is to evaluate how different approaches to modelling and code generation affect embedded systems software development and propose recommendations on how to improve software development. Two product areas at Saab Surveillance EW Systems in Järfälla, Stockholm, are used as study objects. A research overview is made to highlight themes regarding modelling, code generation and software development in general. Based on these, interviews are held with system engineers and software developers at each product area, where they use different modelling and code generation approaches. The two development processes are described thoroughly. Challenges and advantages related to each area’s approach are investigated. Software development within product area A is affected by the product complexity and the larger scale of the development, including projects running over a longer time with more teams involved. Recommendations include enabling code generation by aligning it with other investments on process improvement and limiting the approach to generating some system components. Software developers within product area B can use full code generation, enabled by the limited product complexity. The product area is affected by software standards and external requirements on the process. Recommendations include extending the modelling approach to make it easier to trace functionality from system to software level. Conclusions are that both product areas can apply modelling and code generation to more software development activities to improve their respective development processes.

Na busca de maior flexibilidade, agilidade, reuso e consequente redução de custos, esforços tem sido direcionados no sentido de desenvolver métodos e ferramentas de engenharia de sistemas baseados em modelos. Apesar dos avanços recentes, as tecnologias disponíveis ainda despertam dúvidas em relação à sua aplicação prática e seus benefícios. Os maiores obstáculos estão contidos na dificuldade da integração entre ferramentas e troca de informações entre artefatos de diferentes disciplinas. Com o objetivo de comparar a Engenharia Centrado em Documentação (ferramentas CAx) com a Engenharia de Sistemas Baseada em Modelos (MBSE), esse trabalho utilizou um domínio industrial real para extrair, analisar e comparar dados quantitativos e qualitativos do projeto de engenharia. Foi desenvolvido um método de engenharia baseada em modelos com o uso da ferramenta Eclipse para a comparação com o método de engenharia vigente. A linguagem ModelicaML foi utilizada para criar os modelos abstratos enquanto que a ferramenta COMOS® da Siemens® foi utilizada para a realização dos artefatos técnicos multidisciplinares do domínio em estudo. O software OpenModelica foi utilizado para simular o comportamento do sistema a partir da transformação do modelo abstrato para código Modelica com o uso de software escrito em Java. Os dados de engenharia e de gerenciamento do projeto do Sistema de Circulação de Água foram disponibilizados pela General Electric Inspection Technologies GmbH e foram utilizados para a comparação entre os dois métodos analisados. Os testes demonstraram que as ferramentas MBSE necessitam de refinamento, principalmente quando conectam os modelos abstratos às plataformas de execução de projetos. Em contrapartida, MBSE se mostrou uma excelente ferramenta na comunicação entre equipes multidisciplinares, pois proporciona uma linguagem de representação de sistemas abstrata e abrangente. A interligação dos modelos abstratos desenvolvidos em ModelicaML com a plataforma de simulação usando linguagem Modelica foi fundamental na análise e melhor compreensão dos fenômenos envolvidos no processo técnico propiciando um importante avanço na antecipação da detecção de erros em projetos de sistemas de automação. / Achieving more flexibility, agility, reuse and consequently cost reduction in scope of Systems Engineering is an industrial need. In that sense, efforts have been driven to develop Model-Based Systems Engineering tools and methods. Despite of recent progress, there are still doubts in terms of the practical use and benefits. The main issues are related to tool integration and exchange of information between multidisciplinary artifacts. This project is intended to compare Document-Based Engineering (CAx tools) and Model-Based Systems Engineering (MBSE) in scope of Industrial Automation using a real domain. Therefore, a MBSE methodology was developed centered on the Eclipse tool. The ModelicaML language was used to perform abstract modeling while COMOS® from Siemens® was used to develop the multidisciplinary artifacts necessary for the domain under investigation. Furthermore, the OpenModelica environment was used to simulate system and component behavior using object codes generated by a Java tool from the abstract models. The engineering and project management data of the Water Circulation System were made available by General Electric Inspection Technologies GmbH, which served as the case study for this comparison. Tests have shown that the tools involved on this investigation still need further development concerning maturity and exchange of information from abstract models down to domain models. On the other hand, MBSE has proven to be an important tool to match different team approaches and concerns helping on communication using conceptual-wide and abstract symbols. Connecting abstract models from ModelicaML to a simulation environment using Modelica language have been proven to be an important approach to better understand systems behavior and provided an analysis environment for early detection of errors and failures.

With ever-increasing numbers of engine actuators to calibrate within increasingly stringent emissions legislation, the engine mapping and calibration task of identifying optimal actuator settings is much more difficult. The aim of this research is to evaluate the feasibility and effectiveness of the Multidisciplinary Design Optimisation (MDO) frameworks to optimise the multi-attribute steady state engine calibration optimisation problems. Accordingly, this research is concentrated on two aspects of the steady state engine calibration optimisation: 1) development of a sequential Design of Experiment (DoE) strategy to enhance the steady state engine mapping process, and 2) application of different MDO architectures to optimally calibrate the complex engine applications. The validation of this research is based on two case studies, the mapping and calibration optimisation of a JLR AJ133 Jaguar GDI engine; and calibration optimisation of an EU6 Jaguar passenger car diesel engine. These case studies illustrated that: -The proposed sequential DoE strategy offers a coherent framework for the engine mapping process including Screening, Model Building, and Model Validation sequences. Applying the DoE strategy for the GDI engine case study, the number of required engine test points was reduced by 30 – 50 %. - The MDO optimisation frameworks offer an effective approach for the steady state engine calibration, delivering a considerable fuel economy benefits. For instance, the MDO/ATC calibration solution reduced the fuel consumption over NEDC drive cycle for the GDI engine case study (i.e. with single injection strategy) by 7.11%, and for the diesel engine case study by 2.5%, compared to the benchmark solutions.