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Development of a software system from existing components can surely have various benefits, but can also entail a series of problems. One type of problems is caused by a limited exchange of information between the developer and user of a component, i.e. the developer of a componentbased system. A limited exchange of information cannot only require the testing by the user but it can also complicate this tasks, since vital artifacts, source code in particular, might not be available. Self-testing components can be one response in such situation. This paper describes an enhancement of the Self-Testing COTS Components (STECC) Method so that an appropriately enabled component is not only capable of white-box testing its methods but also capable of black-box testing.

This thesis explores the design development and experimental testing of a planar linkage for the Skiboard, a novel snowsports equipment device. The Skiboard, similar to a skateboard in appearance and style of use, combines two short skis with a bindingless board. Its aim is to fill a gap in the snowsports market for a product that offers a wide range of freestyle and trick riding possibilities, beyond those of a snowboard, while being as stable and easy to ride as a pair of skis. While the concept of the Skiboard in itself is simple, the task of designing a mechanism to link the skis to the board is complex. To translate a gradual lean of the rider into a gradual and equal tilting of the skis requires a multi-loop linkage mechanism. The synthesis and analysis of a mechanism for this application was the inspiration for the development of the synthesis-related design tools presented in this thesis. Design methodologies and design software concepts have been developed for use by designers faced with under-defined, “black-box” linkage synthesis problems similar to the Skiboard mechanism synthesis task. A software-based design of experiments setup, called SMAC, is introduced in this thesis and was used throughout the linkage synthesis process for the Skiboard. One promising candidate mechanism, developed and chosen using SMAC, is followed through to the pre-prototyping phase of the design process. PSEO, another, more advanced, software tool for complex and multi-loop linkage synthesis is also presented in the concept stage of development. This type of program has the potential to automate some of the most time-consuming portions of the synthesis and analysis process with the use of a genetic algorithm and curve-matching algorithm. Additionally, it keeps much of the user’s interaction with the design process and the design itself intact, which is something not offered by existing tools incorporating similar levels of automation. Overall, this thesis is an exploration into the field of linkage design, a topic with little crossover between theory and practical design helps. It includes a review of existing synthesis tools and the development of new tools to suit complex applications such as the Skiboard. The design process for the Skiboard linkage mechanism is also presented and illustrates the way in which the creative design process is iterative, progressively informing the designer’s understanding of the functional requirements of the linkage and how to best satisfy them.

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Evolutionary algorithms have proved to be useful for tackling many practical black-box optimization problems. In this thesis, we describe one of the most powerful evolutionary algorithms of today, CMA- ES, and apply it in novel way to solve the problem of tuning multiple coupled PID controllers in combustion engine models. Powered by TCPDF (www.tcpdf.org)

Code clone Discovery Based on Functional Behavior by Ronald M Krawitz 2012 Legacy programs are used for many years and experience many cycles of use-maintenance-use-maintenance-use-etc. Source code or source code functionality is frequently replicated within these programs when it is written, as well as when it is maintained. Over time many different developers with greater or lesser understanding of the source code maintain the source code. Maintenance developers, when they have limited time or lack understanding of the program, frequently resort to short cuts that include cutting and pasting existing code and re-implementing functionality instead of refactoring. This means a specific functionality is often repeated several times, sometimes using different source code. Blocks of replicated source code or source code functionality are called code clones. Removing code clones improves extensibility, maintainability, and reusability of a program in addition to making the program more easily understood. It is generally accepted that four types of code clones exist. Type-1 and Type-2 code clones are comparatively straightforward to locate and tools exist to locate them. However, Type-3 and Type-4 code clones are very difficult to locate with only a few specialized tools capable of locating them with a lower level of precision. This dissertation presents a new methodology that discovered code clones by studying the functional behavior of blocks of code. Code Clone Discovery based on Functional Behavior (FCD) located code clone by comparing how the blocks of code reacted to various inputs. FCD stimulated the code blocks with the same input patterns and compared the resulting outputs. When a significant portion of the outputs matched, those blocks were declared to be a code clone candidate. Manual analysis confirmed that those blocks of code were code clones. Since FCD discovered code clones based on their black-box behavior, the actual source code syntax was irrelevant and manual inspection further confirmed FCD located code clones that included Type-3 and Type-4 code clones which are frequently excluded from code clone detection tools. FCD recognized the code clones regardless of whether or not they use identical code, similar code, or totally dissimilar code. This new technique allows for an improvement in software quality and has the potential to significantly reduce the cost of software over its lifetime.

Biorreatores, que são explorados desde a antiguidade, são sistemas capazes de realizar a fermentação de compostos orgânicos, continuam sendo amplamente utilizados atualmente devido à diversidade de aplicações. Esses sistemas podem operar em diferentes modos de fermentação, entretanto, os mais utilizados são: fermentação contínua, semicontínua e descontínua. Esse último, juntamente com o processo de digestão anaeróbia (ausência de oxigênio), permitem que uma determinada matéria orgânica seja degradada e transformada em biogás, um dos fatores chave para geração de energia limpa. Percebe-se, portanto, que o estudo de biorreatores em modo de operação descontínuo e em processo de digestão anaeróbia é fundamental para o desenvolvimento de pesquisas relacionadas à geração de energia renovável. Para facilitar o entendimento desse processo, alguns autores propuseram estudos baseados na identificação de parâmetros em modelos não-lineares descritivos, do tipo caixa-branca, que hoje são vastamente utilizados na modelagem de biorreatores. A grande limitação dessa abordagem é que o processo de identificação de sistemas utilizando esses modelos pode ser complexo e demorado, ou, ainda, os parâmetros dos sistemas representados podem não ser identificáveis, inviabilizando o procedimento. Tentando amenizar essas dificuldades, propomos neste trabalho a utilização de modelos polinomiais NAR e NARMA do tipo caixa-preta para a modelagem de biorreatores em modo de fermentação descontínua. Modelos caixa-preta representam sistemas reais por meio de sua saída, sem informação sobre os mecanismos internos desse sistema, simplificando a identificação. Frente a esse contexto, o objetivo deste estudo é investigar a predição e, por consequência, realizar o monitoramento da produção de metano utilizando os modelos caixa-preta propostos em sistemas de biorreatores em modo descontínuo e em processo de digestão anaeróbia. Realizamos estudos que abarcam a investigação de dados simulados e de dados reais. Num primeiro momento são propostos modelos polinomiais dos tipos NAR e NARMA. A partir desses modelos são estimados os parâmetros dos sistemas simulados, com e sem ruído na saída, baseados em condições iniciais propostas na literatura, que denominamos Grupo de Controle. Posteriormente realizamos as validações desses modelos. Em seguida, passamos à etapa de investigação do domínio de validade dos modelos caixa-preta propostos, realizando um estudo em que modificamos as condições iniciais do sistema que representa biorreatores em modo de fermentação descontínua. Por fim, utilizamos dados de um experimento real para realizar o processo de estimação de parâmetros e de validação dos modelos. Os resultados mostraram que os modelos polinomiais NAR e NARMA são bastante adequados para predição de metano em biorreatores em modo de fermentação descontínua em processo de digestão anaeróbia, tanto para os dados simulados quanto para os dados reais. / Bioreactors, which are explored since antiquity, are systems that are capable of performing the fermentation of organic compounds. Nowadays, they are widely applied due to its diversity of applications. These systems can operate in different fermentation modes: continuous, fed-batch and batch. This last fermentation method along with the process of anaerobic digestion allow organic matter to be degraded and converted into biogas, which is a key factor for clean energy generation. It is thus realized that the study of bioreactors in batch mode and anaerobic digestion process is crucial to the development of research related to renewable energy generation. For a better understanding of the process, some authors have proposed studies based on parameters identification in descriptive nonlinear models, white-box models, which are widely used in bioreactors modeling. The main limitation of this approach is that the system identification procedure using these models can be complex and time-consuming, or even the parameters of the systems may not be identifiable. In order to overcome these difficulties, we propose in this work the use of black-box polynomial models for bioreactor modeling in batch mode, with NAR and NARMA model structures. Black-box models represent real systems using its output, without explicitly considering the inner mechanisms of the system, simplifying the identification procedure. Thus, the aim of this work is to investigate the prediction and monitoring methane production using the black-box models proposed using bioreactor systems in batch and anaerobic digestion process. The investigation uses numerical simulation and experimental data. At first, polynomial models of the types NAR and NARMA are proposed. The parameters from these models using simulation data with and without noise at the output, based on initial conditions proposed in the literature, are estimated. Subsequently we perform validations of these models. The next step is the study of the validity domain of the proposed black-box models, which is performed by testing many different initial conditions of the system that represents bioreactors in batch fermentation mode. Finally, we used real experimental data to perform the estimation of the parameters from the process and validation of models. The results, both simulated and experimental, indicate that the polynomial models NAR and NARMA are appropriate for prediction of methane fermentation in batch bioreactors.

abstract: Reverse engineering is critical to reasoning about how a system behaves. While complete access to a system inherently allows for perfect analysis, partial access is inherently uncertain. This is the case foran individual agent in a distributed system. Inductive Reverse Engineering (IRE) enables analysis under such circumstances. IRE does this by producing program spaces consistent with individual input-output examples for a given domain-specific language. Then, IRE intersects those program spaces to produce a generalized program consistent with all examples. IRE, an easy to use framework, allows this domain-specific language to be specified in the form of Theorist s, which produce Theory s, a succinct way of representing the program space. Programs are often much more complex than simple string transformations. One of the ways in which they are more complex is in the way that they follow a conversation-like behavior, potentially following some underlying protocol. As a result, IRE represents program interactions as Conversations in order to more correctly model a distributed system. This, for instance, enables IRE to model dynamically captured inputs received from other agents in the distributed system. While domain-specific knowledge provided by a user is extremely valuable, such information is not always possible. IRE mitigates this by automatically inferring program grammars, allowing it to still perform efficient searches of the program space. It does this by intersecting conversations prior to synthesis in order to understand what portions of conversations are constant. IRE exists to be a tool that can aid in automatic reverse engineering across numerous domains. Further, IRE aspires to be a centralized location and interface for implementing program synthesis and automatic black box analysis techniques. / Dissertation/Thesis / Masters Thesis Computer Science 2019

The European emission standard is an EU directive which describes what emission limits car manufactures are required to meet. In order to meet these requirements car manufacturers use different techniques and components. In a modern diesel automobile a Diesel Particulate Filter (DPF) is used to gather soot from the exhausts. As soot accumulates in the DPF, the back pressure increases and the capability to hold more soot decreases. Therefore the DPF continuously needs to get rid of the stored soot. The soot is removed through a process called regeneration. In order to optimize when to perform regeneration, it is vital to know the amount of soot in the filter. A method for estimating the soot mass in a DPF using a radio frequency-based sensor has been developed. The sensor that has been studied is the Accusolve soot sensor from General Electric. A parameter study has been performed to evaluate the parameters that affects the sensor’s output. Parameters that have been studied include positioning of the sensor, temperature in the DPF, flow rate through the DPF and distribution of soot in the DPF. Different models for estimation of soot mass in the DPF has been developed and analyzed. An uncertainty caused by removing the coaxial cable connectors when weighing the DPF has been identified and methods for minimizing this uncertainty has been presented. Results show that the sensor output is sensitive to temperature, soot distribution and position, and also show some sensitivity to the flow rate. An ARX model, with only one state, is proposed to estimate the soot mass in the DPF, since it gives the best prediction of soot mass and showed good resistance to bias errors and noise in all the input signals.

In recent year, a lot of scholar try to generate test sets for combinatorial strategy automatically. But these algorithms based on combinatorial strategy don¡¦t consider conflicts of input parameter model. A conflict exists when the result of combining two or more values of different parameter dose not make sense. Thus, invalid sub-combinations may be included in test cases in the test suite, and these are useless to us. Besides, these algorithms all directly generate all test cases once, in other words, it is unable to utilize test cases generated at present to feedback and revise the algorithm, so it is easy to generate useless combinations. So, this paper proposes new test generation algorithm for combinatorial testing based on constraint satisfaction problem(CSP) to solve problem which invalid sub-combinations may be included in test cases, and we can add constraints flexibly during generating test cases to avoid generate useless or repeated combinations. The experimental result indicate that our algorithm perform well, with respect to the amount of time required for test generation, otherwise, we can generate conflict-free test cases directly.