Submitted by Neusa Fagundes (neusa.fagundes@unioeste.br) on 2018-09-10T17:23:20Z No. of bitstreams: 2 Tamara_Maltauro2018.pdf: 3146012 bytes, checksum: 16eb0e2ba58be9d968ba732c806d14c1 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) / Made available in DSpace on 2018-09-10T17:23:20Z (GMT). No. of bitstreams: 2 Tamara_Maltauro2018.pdf: 3146012 bytes, checksum: 16eb0e2ba58be9d968ba732c806d14c1 (MD5) license_rdf: 0 bytes, checksum: d41d8cd98f00b204e9800998ecf8427e (MD5) Previous issue date: 2018-02-21 / Coordenação de Aperfeiçoamento de Pessoal de Nível Superior - CAPES / It is essential to determine a sampling design with a size that minimizes operating costs and maximizes the results quality throughout a trial setting that involves the study of spatial variability of chemical attributes on soil. Thus, this trial aimed at resizing a sample configuration with the least possible number of points for a commercial area composed of 102 points, regarding the information on spatial variability of soil chemical attributes to optimize the process. Initially, Monte Carlo simulations were carried out, assuming Gaussian, isotropic, and exponential model for semi-variance function and three initial sampling configurations: systematic, simple random and lattice plus close pairs. The Genetic Algorithm (GA) was used to obtain simulated data and chemical attributes of soil, in order to resize the optimized sample, considering two objective-functions. They are based on the efficiency of spatial prediction and geostatistical model estimation, which are respectively: maximization of global accuracy precision and minimization of functions based on Fisher information matrix. It was observed by the simulated data that for both objective functions, when the nugget effect and range varied, samplings usually showed the lowest values of objectivefunction, whose nugget effect was 0 and practical range was 0.9. And the increase in practical range has generated a slight reduction in the number of optimized sampling points for most cases. In relation to the soil chemical attributes, GA was efficient in reducing the sample size with both objective functions. Thus, sample size varied from 30 to 35 points in order to maximize global accuracy precision, which corresponded to 29.41% to 34.31% of the initial mesh, with a minimum spatial prediction similarity to the original configuration, equal to or greater than 85%. It is noteworthy that such data have reflected on the optimization process, which have similarity between the maps constructed with sample configurations: original and optimized. Nevertheless, the sample size of the optimized sample varied from 30 to 40 points to minimize the function based on Fisher information matrix, which corresponds to 29.41% and 39.22% of the original mesh, respectively. However, there was no similarity between the constructed maps when considering the initial and optimum sample configuration. For both objective functions, the soil chemical attributes showed mild spatial dependence for the original sample configuration. And, most of the attributes showed mild or strong spatial dependence for optimum sample configuration. Thus, the optimization process was efficient when applied to both simulated data and soil chemical attributes. / É necessário determinar um esquema de amostragem com um tamanho que minimize os custos operacionais e maximize a qualidade dos resultados durante a montagem de um experimento que envolva o estudo da variabilidade espacial de atributos químicos do solo. Assim, o objetivo deste trabalho foi redimensionar uma configuração amostral com o menor número de pontos possíveis para uma área comercial composta por 102 pontos, considerando a informação sobre a variabilidade espacial de atributos químicos do solo no processo de otimização. Inicialmente, realizaram-se simulações de Monte Carlo, assumindo as variáveis estacionárias Gaussiana, isotrópicas, modelo exponencial para a função semivariância e três configurações amostrais iniciais: sistemática, aleatória simples e lattice plus close pairs. O Algoritmo Genético (AG) foi utilizado para a obtenção dos dados simulados e dos atributos químicos do solo, a fim de se redimensionar a amostra otimizada, considerando duas funções-objetivo. Essas estão baseadas na eficiência quanto à predição espacial e à estimação do modelo geoestatístico, as quais são respectivamente: a maximização da medida de acurácia exatidão global e a minimização de funções baseadas na matriz de informação de Fisher. Observou-se pelos dados simulados que, para ambas as funções-objetivo, quando o efeito pepita e o alcance variaram, em geral, as amostragens apresentaram os menores valores da função-objetivo, com efeito pepita igual a 0 e alcance prático igual a 0,9. O aumento do alcance prático gerou uma leve redução do número de pontos amostrais otimizados para a maioria dos casos. Em relação aos atributos químicos do solo, o AG, com ambas as funções-objetivo, foi eficiente quanto à redução do tamanho amostral. Para a maximização da exatidão global, tem-se que o tamanho amostral da nova amostra reduzida variou entre 30 e 35 pontos que corresponde respectivamente a 29,41% e a 34,31% da malha inicial, com uma similaridade mínima de predição espacial, em relação à configuração original, igual ou superior a 85%. Vale ressaltar que tais dados refletem no processo de otimização, os quais apresentam similaridade entres os mapas construídos com as configurações amostrais: original e otimizada. Todavia, o tamanho amostral da amostra otimizada variou entre 30 e 40 pontos para minimizar a função baseada na matriz de informaçãode Fisher, a qual corresponde respectivamente a 29,41% e 39,22% da malha original. Mas, não houve similaridade entre os mapas elaborados quando se considerou a configuração amostral inicial e a otimizada. Para ambas as funções-objetivo, os atributos químicos do solo apresentaram moderada dependência espacial para a configuração amostral original. E, a maioria dos atributos apresentaram moderada ou forte dependência espacial para a configuração amostral otimizada. Assim, o processo de otimização foi eficiente quando aplicados tanto nos dados simulados como nos atributos químicos do solo.

Accurate and reliable effort estimation is still one of the most challenging processes in software engineering. There have been numbers of attempts to develop cost estimation models. However, the evaluation of model accuracy and reliability of those models have gained interest in the last decade. A model can be finely tuned according to specific data, but the issue remains there is the selection of the most appropriate model. A model predictive accuracy is determined by the difference of the various accuracy measures. The one with minimum relative error is considered to be the best fit. The model predictive accuracy is needed to be statistically significant in order to be the best fit. This practice evolved into model evaluation. Models predictive accuracy indicators need to be statistically tested before taking a decision to use a model for estimation. The aim of this thesis is to statistically evaluate well known effort estimation models according to their predictive accuracy indicators using two new approaches; bootstrap confidence intervals and permutation tests. In this thesis, the significance of the difference between various accuracy indicators were empirically tested on the projects obtained from the International Software Benchmarking Standard Group (ISBSG) data set. We selected projects of Un-Adjusted Function Points (UFP) of quality A. Then, the techniques; Analysis Of Variance ANOVA and regression to form Least Square (LS) set and Estimation by Analogy (EbA) set were used. Step wise ANOVA was used to form parametric model. K-NN algorithm was employed in order to obtain analogue projects for effort estimation use in EbA. It was found that the estimation reliability increased with the pre-processing of the data statistically, moreover the significance of the accuracy indicators were not only tested statistically but also with the help of more complex inferential statistical methods. The decision of selecting non-parametric methodology (EbA) for generating project estimates in not by chance but statistically proved.