Sound quality analysis is a tool designed to help determine customer preferences, which can be used to help the designer improve product quality. Many industries desire to know how the consuming public perceives their product, as this affects the product life and success. This research investigates which of the six sewing machines provided by Viking Sewing Machine Group (VSM group) consumers find most acoustically appealing. The sound quality analysis methods used include both jury based listening tests and quantitative sound quality metrics from empirical equations. The results from both methods are completely independent and are shown to have a very strong correlation. The procedures and results of both methods, jury listening tests and mathematical metrics, are presented. Near field sound intensity scans identified acoustic hot spots and give direction for possible design modifications to improve the acoustic signature of the two top tier machines, the Designer 1 and Creative 2144 (Husqvarna Viking and Pfaff respectively). This research determined that the entry level Pfaff Select 1530 has the most acoustically appealing sound of the six machines evaluated. In addition, it was also determined that a reduction in the higher frequency sounds produced by the machines is preferred over a reduction in the lower frequency sounds. Further investigations, including an evaluation of machine isolation and startup sounds, were also performed. The machine isolation results are highly dependant on the individual machine being evaluated and would require independent evaluation. In the machine startup sound assessment, it was discovered that again the Pfaff Select 1530 has the preferred sound. Near field acoustic intensity scans provide additional information on locations of strong acoustic radiation. The near field scans provided valuable design information. The acoustic "hot" spots were discovered to exist in the lower portions of the machines near the main stepper motor in the Designer 1, and radiating from the bottom plate of the machine in the Pfaff Creative 2144. This analysis has led to various design modifications that could be implemented to improve the sound quality of the machines, specifically the Designer 1 and the Creative 2144.

E-learning systems provide a promising solution as an information exchanging channel. Improved technology could mean faster and easier access to information but does not necessarily ensure the quality of this information. Therefore it is essential to develop valid and reliable methods of quality measurement and carry out careful information quality evaluations. Information quality frameworks are developed to measure the quality of information systems, generally from the designers¿ viewpoint. The recent proliferation of e-services, and e-learning particularly, raises the need for a new quality framework in the context of e-learning systems. The main contribution of this thesis is to propose a new information quality framework, with 14 information quality attributes grouped in three quality dimensions: intrinsic, contextual representation and accessibility. We report results based on original questionnaire data and factor analysis. Moreover, we validate the proposed framework using an empirical approach. We report our validation results on the basis of data collected from an original questionnaire and structural equation modeling (SEM) analysis, confirmatory factor analysis (CFA) in particular. However, it is difficult to measure information quality in an e-learning context because the concept of information quality is complex and it is expected that the measurements will be multidimensional in nature. Reliable measures need to be obtained in a systematic way, whilst considering the purpose of the measurement. Therefore, we start by adopting a Goal Question Metrics (GQM) approach to develop a set of quality metrics for the identified quality attributes within the proposed framework. We then define an assessment model and measurement scheme, based on a multi element analysis technique. The obtained results can be considered to be promising and positive, and revealed that the framework and assessment scheme could give good predictions for information quality within e-learning context. This research generates novel contributions as it proposes a solution to the problems raised from the absence of consensus regarding evaluation standards and methods for measuring information quality within an e-learning context. Also, it anticipates the feasibility of taking advantage of web mining techniques to automate the retrieval process of the information required for quality measurement. This assessment model is useful to e-learning systems designers, providers and users as it gives a comprehensive indication of the quality of information in such systems, and also facilitates the evaluation, allows comparisons and analysis of information quality.

Software quality attributes are properties that reflect the quality of a software system, and Non-functional requirements (NFRs) are the specifications that define how a software system should perform to reach a desired level of goals of the quality attributes.The evaluation of quality attributes is important to show the effectiveness of a system in meeting customers' NFRs. Continuous integration (CI) environments have emerged as powerful platforms for organizations to improve software quality through automated software verification and validation.Despite this, there is a growing need for evaluating quality attributes that is often met by in-house development of metrics and tools.This highlights the importance of quality attributes for software product quality. This thesis investigates the association between quality attributes and components of a CI environment, as well as how to utilize these components for evaluating software quality attributes.The focus is on improving the knowledge of the evaluation and providing specific recommendations for companies to enhance their CI environments for higher demands of quality evaluation.The contributions of this thesis include a better understanding of the relationship between quality attributes and CI components, and a set of practical guidelines for companies to effectively leverage CI for quality attribute evaluation. The studies in this thesis utilized mixed methodologies, including a systematic literature review, a multi-case study conducted in four software development companies, and an synthesis of the collected data.The multi-case study provided a comprehensive overview of practices for quality attribute evaluation and how CI components can generate data to support the evaluation of specific attributes.The synthesis study presents a maturity model based on the collected data from both academia and industry, and the model can aid organizations in assessing their current level of maturity in utilizing CI environments to identify potential improvements.The results in these studies show the capabilities of different components of a CI environment and how these components can be used to support the evaluation of quality attributes.While the use of CI environments for the thesis topic offers benefits, it also presents several challenges, for example, the challenge to identify effective quality metrics. In conclusion, this thesis contributes to the understanding of the use of CI environments for evaluating software quality attributes.The results suggest that CI environments can be an effective approach for quality attribute evaluation, but suitable metrics need to be considered to ensure accurate and meaningful evaluation results. Furthermore, the thesis presents areas for future research, such as the use of machine learning techniques to improve the accuracy of quality assessment using CI environments.

Cattle exclusion from streams is believed to improve riparian vegetation, in-stream habitat, and composition of aquatic organisms. Yet research on the effects of cattle exclusion have yielded conflicting results. The goal of this study was to examine relationships between physical habitat and benthic macroinvertebrate populations with increasing downstream distance from cattle-impacted stream segments, and determine which physical habitat and chemical water quality parameters are affected by cattle presence. Macroinvertebrates from 24 sites in Rockingham County, VA were used to calculate bioassessment metrics. Fourteen sites made up 4 longitudinal studies where improvement of biotic condition with distance from cattle impact was examined. Linear regression and multilevel modeling results indicated improving macroinvertebrate assemblage with increasing distance downstream from cattle-impacted reaches. Presence of riparian trees and distance from impact had a positive influence on bioassessment scores. A total of 39 stream sites in the Shenandoah Valley were classified using the Rapid Habitat Assessment (RHA) which is based on 10 visual evaluations of physical characteristics. Four of the ten RHA parameters, embeddedness, bank stability, vegetative protection, and riparian vegetative zone width, along with the total RHA score, were associated with cattle presence. This study found that a) RHA factors reflect direct cattle impacts on the riparian zone, but RHA has limitations as a general predictor of cattle impact, b) cattle influence on benthic macroinvertebrates extends hundreds of meters beyond the immediate pasture boundary, and c) improvement in Virginia Stream Condition Index can be predicted as a function of distance downstream. / Master of Science

Neste trabalho é apresentado um método probabilístico para avaliação do desempenho de redes de distribuição considerando incertezas na demanda das cargas e na potência gerada por sistemas distribuídos intermitentes. Os consumidores são divididos em agrupamentos por classe e faixa de consumo e a modelagem da demanda horária dos consumidores de cada agrupamento é realizada por uma lei de distribuição acumulada de probabilidade (CDF) adequada. A geração distribuída é contemplada pela consideração de fonte solar fotovoltaica. O procedimento de simulação do Método de Monte Carlo é empregado e a técnica da Joint Normal Transform é utilizada na geração de números aleatórios correlacionados, empregados na amostragem da demanda dos consumidores e da energia produzida pelos sistemas de geração distribuídos. O método proposto foi aplicado ao conhecido sistema de 13 barras do IEEE e os resultados dos indicadores de perdas na operação bem como indicadores de violação de tensão crítica e precária obtidos com o modelo probabilístico são comparados aos obtidos com o modelo determinístico convencional. É demonstrado que nem sempre a média é uma descrição suficiente para o comportamento dos componentes de redes de distribuição e que é mais adequado utilizar uma representação com intervalos de confiança para as grandezas de interesse. / This work presents a probabilistic method for performance evaluation of distribution networks considering uncertainties in load demand and power generated by intermittent distributed systems. Consumers are divided into clusters by class and consumption range, so the modeling for the hourly demand of the consumers on each cluster is performed by a suitable cumulative probability distribution (CDF). Distributed generation is considered by means of solar photovoltaic sources. The Monte Carlo Simulation (MCS) Method is employed and the Joint Normal Transform technique is applied for correlated random numbers generation, used to sample consumer demand and the energy generated by distributed generation systems. The proposed method was applied in the well-known IEEE 13 node test feeder and the results of the operation losses as well as voltage violation indices obtained by the probabilistic model are compared to those obtained with the conventional deterministic model. It is shown that the mean is not always a sufficient description for the behavior of distribution network components and that it is more appropriate to use confidence intervals for the quantities of interest.

Software reuse is a powerful tool in order to reduce development and maintenance time and cost. Any software life cycle product can be reused, not only fragments of source code. A high degree of reuse correlates with a low defect density. In the literature, many theoretical and empirical researches have examined the relationship of software reuse and quality. In this thesis, the effects of reuse on software quality are investigated in an industrial setting. Throughout this study, we worked with Turkey&rsquo / s leading defense industry company: Aselsan&rsquo / s software engineering department. We aimed to explore their real-life software projects and interpret reuse and quality relations for their projects. With this intention, we defined four different hypotheses to determine reuse and quality relations / and in order to confirm these hypotheses / we designed three separate case studies. In these case studies, we collected and calculated reuse and quality metrics i.e. Object-oriented quality metrics, reuse rates and performance measures of individual modules, fault-proneness of software components, and productivity rates of different products. Finally, by analyzing these measurements, we developed suggestions to further benefit from reuse in Aselsan through systematic improvements to the reuse infrastructure and process. Similar case studies have been reported in the literature, however, in Turkey, there are not many case studies using real-life project data, particularly in the defense industry.

While software subcontracting is a common business transaction in the information technology field, metrics specifically aimed at effectiveness of software subcontracting arrangements are not commonly available. This thesis makes a review of the literature and derives such metrics from fields of software quality, COTS acquisition and IS success. A case study is performed on software subcontracting projects of a Turkish defense contractor, and the project metrics are compared according to their success. The results suggest that metrics regarding the requirements are good indicators for subcontracting success and that larger projects may enjoy more success compared to smaller ones.

The main objective of this thesis is to identify how the software repository features influence software quality during software evolution. To do that the mining software repository area was used. This field analyzes the rich data from software repositories to extract interesting and actionable information about software systems, projects and software engineering. The ability to measure code quality and analyze the impact of software repository features on software quality allows us to better understand project history, project quality state, development processes and conduct future project analysis. Existing work in the area of software quality describes software quality analysis without a connection to the software repository features. Thus they lose important information that can be used for preventing bugs, decision-making and optimizing development processes. To conduct the analysis specific tool was developed, which cover quality measurement and repository features extraction. During the research general procedure of the software quality analysis was defined, described and applied in practice. It was found that there is no most influential repository feature and the correlation between software quality and software repository features exist, but it is too small to make a real influence.

A complexidade dos dispositivos embarcados propõe novos desafios para o desenvolvimento de software embarcado, além das tradicionais restrições físicas. Então, a avaliação da qualidade do software embarcado e seu impacto nessas propriedades tradicionais torna-se mais importante. Conceitos como reúso abstração, coesão, acoplamento, entre outros atributos de software têm sido usados como métricas de qualidade no domínio da engenharia de software. No entanto, elas não têm sido usadas no domínio do software embarcado. No desenvolvimento de sistemas embarcados outro conjunto de ferramentas é usado para estimar as propriedades físicas, tais como: consumo de energia, ocupação de memória e desempenho. Essas ferramentas geralmente envolvem custosos processos de síntese e simulação. Nos complexos dispositivos embarcados atuais deve-se confiar em ferramentas que possam ajudar na exploração do espaço de projeto ainda nos níveis mais altos de abstração, identificando a solução que representa a melhor estratégia de projeto em termos da qualidade de software, enquanto, simultaneamente, atenda aos requisitos físicos. Neste trabalho é apresentada uma análise da correlação entre métricas de qualidade de software, que podem ser extraídas antes do sistema ser sintetizado, e as métricas físicas do software embarcado. Usando uma rede neural nós investigamos o uso dessas correlações para predizer o impacto que uma determinada modificação no software trará às métricas físicas do mesmo software. Esta estimativa pode ser usada para guiar decisões em direção a melhoria das propriedades físicas dos sistemas embarcados, além de manter um equilíbrio em relação às métricas de software. / The complexity of embedded devices poses new challenges to embedded software development in addition to the traditional physical requirements. Therefore, the evaluation of the quality of embedded software and its impact on these traditional properties becomes increasingly relevant. Concepts such as reuse, abstraction, cohesion, coupling, and other software attributes have been used as quality metrics in the software engineering domain. However, they have not been used in the embedded software domain. In embedded systems development, another set of tools is used to estimate physical properties such as power consumption, memory footprint, and performance. These tools usually require costly synthesis-and-simulation design cycles. In current complex embedded devices, one must rely on tools that can help design space exploration at the highest possible level, identifying a solution that represents the best design strategy in terms of software quality, while simultaneously meeting physical requirements. We present an analysis of the cross-correlation between software quality metrics, which can be extracted before the final system is synthesized, and physical metrics for embedded software. Using a neural network, we investigate the use of these cross-correlations to predict the impact that a given modification on the software solution will have on embedded software physical metrics. This estimation can be used to guide design decisions towards improving physical properties of embedded systems, while maintaining an adequate trade-off regarding software quality.

A complexidade dos dispositivos embarcados propõe novos desafios para o desenvolvimento de software embarcado, além das tradicionais restrições físicas. Então, a avaliação da qualidade do software embarcado e seu impacto nessas propriedades tradicionais torna-se mais importante. Conceitos como reúso abstração, coesão, acoplamento, entre outros atributos de software têm sido usados como métricas de qualidade no domínio da engenharia de software. No entanto, elas não têm sido usadas no domínio do software embarcado. No desenvolvimento de sistemas embarcados outro conjunto de ferramentas é usado para estimar as propriedades físicas, tais como: consumo de energia, ocupação de memória e desempenho. Essas ferramentas geralmente envolvem custosos processos de síntese e simulação. Nos complexos dispositivos embarcados atuais deve-se confiar em ferramentas que possam ajudar na exploração do espaço de projeto ainda nos níveis mais altos de abstração, identificando a solução que representa a melhor estratégia de projeto em termos da qualidade de software, enquanto, simultaneamente, atenda aos requisitos físicos. Neste trabalho é apresentada uma análise da correlação entre métricas de qualidade de software, que podem ser extraídas antes do sistema ser sintetizado, e as métricas físicas do software embarcado. Usando uma rede neural nós investigamos o uso dessas correlações para predizer o impacto que uma determinada modificação no software trará às métricas físicas do mesmo software. Esta estimativa pode ser usada para guiar decisões em direção a melhoria das propriedades físicas dos sistemas embarcados, além de manter um equilíbrio em relação às métricas de software. / The complexity of embedded devices poses new challenges to embedded software development in addition to the traditional physical requirements. Therefore, the evaluation of the quality of embedded software and its impact on these traditional properties becomes increasingly relevant. Concepts such as reuse, abstraction, cohesion, coupling, and other software attributes have been used as quality metrics in the software engineering domain. However, they have not been used in the embedded software domain. In embedded systems development, another set of tools is used to estimate physical properties such as power consumption, memory footprint, and performance. These tools usually require costly synthesis-and-simulation design cycles. In current complex embedded devices, one must rely on tools that can help design space exploration at the highest possible level, identifying a solution that represents the best design strategy in terms of software quality, while simultaneously meeting physical requirements. We present an analysis of the cross-correlation between software quality metrics, which can be extracted before the final system is synthesized, and physical metrics for embedded software. Using a neural network, we investigate the use of these cross-correlations to predict the impact that a given modification on the software solution will have on embedded software physical metrics. This estimation can be used to guide design decisions towards improving physical properties of embedded systems, while maintaining an adequate trade-off regarding software quality.