Testing every possible combination of the input parameter values is often impractical, inefficient or too expensive. One common alternative is pairwise testing where every pairwise combination of the parameter values is tested. Although pairwise testing significantly reduces the number of test cases, the challenge remains in analyzing the test outputs to discern the precise characteristics of parameters causing the failures. This thesis proposes a novel approach to output analysis by identifying “bad pairs”: pairs that always result in failed test cases. A framework implementing the proposed approach is presented together with three case studies. Results from the case studies suggest there are positive relationships among the numbers of failed test cases, faults, and independent bad pairs. Also, filtering of test cases seems to have a significant impact on the bad pairs identified. We believe the proposed approach can facilitate the debugging process in software testing.

Submitted by Joana Vieira (joana_cvieira@hotmail.com) on 2015-10-13T12:58:25Z No. of bitstreams: 1 FINAL REPORT JOANA VEIRA FGV.pdf: 3927213 bytes, checksum: a8f998809220a76b7f10b84fa630e2b0 (MD5) / Rejected by Ana Luiza Holme (ana.holme@fgv.br), reason: Joana, o numero das paginas deve contar a partir da capa, está certo mas só deve aparecer o numero a partir da introdução. fico a disposição. Ana Luiza Holme 3799-3492 on 2015-10-13T16:24:51Z (GMT) / Submitted by Joana Vieira (joana_cvieira@hotmail.com) on 2015-10-13T17:42:24Z No. of bitstreams: 1 FINAL REPORT JOANA VEIRA FGV.pdf: 3951971 bytes, checksum: 667456b57a07dda99ac9aeb852a0c8ee (MD5) / Approved for entry into archive by Ana Luiza Holme (ana.holme@fgv.br) on 2015-10-13T17:44:14Z (GMT) No. of bitstreams: 1 FINAL REPORT JOANA VEIRA FGV.pdf: 3951971 bytes, checksum: 667456b57a07dda99ac9aeb852a0c8ee (MD5) / Made available in DSpace on 2015-10-13T17:49:57Z (GMT). No. of bitstreams: 1 FINAL REPORT JOANA VEIRA FGV.pdf: 3951971 bytes, checksum: 667456b57a07dda99ac9aeb852a0c8ee (MD5) Previous issue date: 2015-09-25 / Taking into account previous research we could assume to be beneficial to diversify investments in emerging economies. We investigate in the paper International Portfolio Diversification: evidence from Emerging Markets if it still holds true, given the assumption of larger world markets integration. Our results suggest a wide spread positive time-varying correlations of emerging and developed markets. However, pair-wise cross-country correlations gave evidence that emerging markets have low integration with developed markets. Consequently, we evaluate out-of-sample performance of a portfolio with emerging equity countries, confirming the initial statement that it has a better a risk-adjusted performance over a purely developed markets portfolio.

Os bandos mistos de aves são agregações sociais complexas estáveis durante o tempo e espaço. Até hoje, a estrutura social dessas espécies foi descrita a partir de estudos subjetivos de campo ou a partir de compilações do número e intensidade das interações a nível de todo o grupo, sem considerar as interações par-a-par individualmente. Nossos objetivos foram buscar evidências de associações não-randômicas entre pares de espécies de aves, delimitar os grupos a partir das espécies com as associações mais fortes e verificar se há diferenças na estrutura social entre os habitat de floresta primária e secundária. Utilizamos dados de ocorrência das espécies coletados a partir de redes de neblina e gravação de vocalizações para identificar pares de espécies que foram co-detectadas mais frequentemente do que o esperado a partir do modelo nulo e compararamos a força dessa interação entre as florestas tropicais primária e secundária Amazônicas. Nós também utilizamos as associações par-a-par para construir as redes de interação social e suas mudanças entre os tipos de habitat. Nós encontramos muitas interações positivas fortes entre as espécies, mas nenhuma evidência de repulsão. As análises das redes de interação revelaram vários grupos de espécies que corroboram com grupos ecológios descritos na literatura. Além disso, tanto a estrutura da rede de interação como a força da interação se alteraram drasticamente com a perturbação do habitat, com formação de algumas associações novas, mas uma tendência geral para quebra de associações entre as espécies. Nossos resultados mostram que as interações sociais entre essas aves podem ser fortemente afetados pela degradação do habitat, sugerindo que a estabilidade das interações desenvolvida entre espécies é ameaçada pelos distúrbios causados pelo homem. / Inter-specific associations of birds are complex social phenomena, frequently detected and often stable over time and space. So far, the social structure of these associations has been largely deduced from subjective assessments in the field or by counting the number of inter-specific encounters at the whole-group level, without considering changes to individual pairwise interactions. Here, we look for evidence of non-random association between pairs of bird species, delimit groups of more strongly associated species and examine differences in social structure between old growth and secondary forest habitat. We used records of bird species detection from mist-netting capture and from acoustic recordings to identify pairwise associations that were detected more frequently than expected under a null distribution, and compared the strength of these associations between old-growth and secondary forest Amazonian tropical forest. We also used the pairwise strength associations to visualize the social network structure and its changes between habitat types. We found many strongly positive interactions between species, but no evidence of repulsion. Network analyses revealed several modules of species that broadly agree with the subjective groupings described in the ornithological literature. Furthermore, both network structure and association strength changed drastically with habitat disturbance, with the formation of a few new associations but a general trend towards the breaking of associations between species. Our results show that social grouping in birds is real and may be strongly affected by habitat degradation, suggesting that the stability of the associations is threatened by anthropogenic disturbance.

Os bandos mistos de aves são agregações sociais complexas estáveis durante o tempo e espaço. Até hoje, a estrutura social dessas espécies foi descrita a partir de estudos subjetivos de campo ou a partir de compilações do número e intensidade das interações a nível de todo o grupo, sem considerar as interações par-a-par individualmente. Nossos objetivos foram buscar evidências de associações não-randômicas entre pares de espécies de aves, delimitar os grupos a partir das espécies com as associações mais fortes e verificar se há diferenças na estrutura social entre os habitat de floresta primária e secundária. Utilizamos dados de ocorrência das espécies coletados a partir de redes de neblina e gravação de vocalizações para identificar pares de espécies que foram co-detectadas mais frequentemente do que o esperado a partir do modelo nulo e compararamos a força dessa interação entre as florestas tropicais primária e secundária Amazônicas. Nós também utilizamos as associações par-a-par para construir as redes de interação social e suas mudanças entre os tipos de habitat. Nós encontramos muitas interações positivas fortes entre as espécies, mas nenhuma evidência de repulsão. As análises das redes de interação revelaram vários grupos de espécies que corroboram com grupos ecológios descritos na literatura. Além disso, tanto a estrutura da rede de interação como a força da interação se alteraram drasticamente com a perturbação do habitat, com formação de algumas associações novas, mas uma tendência geral para quebra de associações entre as espécies. Nossos resultados mostram que as interações sociais entre essas aves podem ser fortemente afetados pela degradação do habitat, sugerindo que a estabilidade das interações desenvolvida entre espécies é ameaçada pelos distúrbios causados pelo homem. / Inter-specific associations of birds are complex social phenomena, frequently detected and often stable over time and space. So far, the social structure of these associations has been largely deduced from subjective assessments in the field or by counting the number of inter-specific encounters at the whole-group level, without considering changes to individual pairwise interactions. Here, we look for evidence of non-random association between pairs of bird species, delimit groups of more strongly associated species and examine differences in social structure between old growth and secondary forest habitat. We used records of bird species detection from mist-netting capture and from acoustic recordings to identify pairwise associations that were detected more frequently than expected under a null distribution, and compared the strength of these associations between old-growth and secondary forest Amazonian tropical forest. We also used the pairwise strength associations to visualize the social network structure and its changes between habitat types. We found many strongly positive interactions between species, but no evidence of repulsion. Network analyses revealed several modules of species that broadly agree with the subjective groupings described in the ornithological literature. Furthermore, both network structure and association strength changed drastically with habitat disturbance, with the formation of a few new associations but a general trend towards the breaking of associations between species. Our results show that social grouping in birds is real and may be strongly affected by habitat degradation, suggesting that the stability of the associations is threatened by anthropogenic disturbance.

Objetivo. Comparar, teórica e empiricamente, dois estimadores do coeficiente de correlação intraclasse momento-produto de Pearson para pares repetidos Pi. O primeiro é o estimador \"natural\", obtido mediante a correlação momento-produto de Pearson para membros de uma mesma classe (rI) e o segundo, obtido como função de componentes de variância (icc). Métodos. Comparação teórica e empírica dos parâmetros e estimadores. A comparação teórica envolve duas definições do coeficiente de correlação intraclasse PI como medida de confiabilidade (*), para o caso de duas réplicas, assim como uma apresentação da técnica de análise de variância e a definição e interpretação dos estimadores ri e icc. A comparação empírica é realizada mediante um estudo de simulação Monte Carlo com a geração de pares de valores correlacionados segundo o coeficiente de correlação intraclasse, momento-produto de Pearson para pares repetidos. Os pares de valores são distribuídos segundo uma distribuição Normal bivariada, com valores do tamanho da amostra e da correlação intraclasse previamente fixados em: n= 15, 30 e 45 e pI = {O; 0,15; 0,30; 0,45; 0,60; 0,75; 0,9}. Resultados. Comparando-se o vício e o erro quadrático médio dos estimadores, bem como as amplitudes dos intervalos de confiança, tem-se como resultado que o vício de icc foi sempre menor que o vício de rI, mesmo ocorrendo com o erro quadrático médio. Conclusões. O icc é um estimador melhor, principalmente para n pequeno (por exemplo 15). Para valores maiores de n (30 ou mais), os estimadores produzem resultados iguais até a segunda casa decimal. / Objective. This thesis presents and compares, theoretically and empirically, two estimators of the intraclass correlation coefficient pI, defined as Pearson\'s pairwise intraclass correlation coefficient. The first is the \"natural\" estimator, obtained by Pearson\'s moment-product correlation for members of one class (rI) while the second was obtained as a function of components of variance (icc). Methods. Theoretical and empirical comparison of the parameters and estimators are performed. The theoretical comparison involves two definitions of the intrac1ass correlation coefficient pI as a measure of reliability (*) for two repeated measurements in the same class and the presentation of the technique of analysis of variance, as well as for the definition and interpretation of the estimators ri and icc. The empirical comparison was carried out by means of a Monte Carlo simulation study of pairs of correlated values according Pearson\'s pairwise correlation. The pairs of values follow a normal bivariate distribution, with correlation values and sample size previously fixed: n= 15, 30 e 45 and Pl = . Results. Bias and mean square error for the estimators were compared as well as the range of the intervals of confidence. The comparison shows that the bias of icc is always smaller than of rI This also applies to the mean square error. Conclusions. The icc is a better estimator, especially for n less than or equal to 15. For larger samples sízes (n 30 or more), the estimators produce results that are equal to the second decimal place. (*) Fórmula

Abstract This thesis presents computational studies of both the static and dynamic parameters of the nuclear magnetic resonance (NMR) spectroscopy of gaseous xenon. First, state-of-the-art static magnetic resonance parameters are computed in small xenon clusters by using methods of quantum chemistry, and second, time-dependent relaxation phenomena are investigated via molecular dynamics simulations at different experimental conditions. Based on the underlying quantum and classical mechanics concepts, computational methods represent a procedure complementary to experiments for investigating the properties of atoms, molecules, clusters and solids. Static NMR spectral parameters, chemical shift, shielding anisotropy and asymmetry parameter, nuclear quadrupole coupling, and spin-rotation coupling, are calculated using different electronic structure methods ranging from the uncorrelated Hartree-Fock method to correlated second-order Møller-Plesset many-body perturbation, complete/restricted active space multiconfiguration self-consistent field, and to coupled-cluster approaches. The bond length dependence of these properties is investigated in the xenon dimer (Xe2). A well-characterized property in experimental NMR, the second virial coefficient of nuclear shielding, is theoretically calculated by a variety of methods and convincingly verified against experimental findings. Here, it is mandatory to include effects from special relativity as well as electron correlation. As a side result, a purely theoretical potential energy curve for Xe2, comparable to best experimental ones, is calculated. A pairwise additive scheme is established to approximate the NMR properties in differently coordinated sites of xenon clusters Xen (n = 2 - 12). Especially the pairwise additive chemical shift values are found to be in close agreement with quantum-chemical results and only a small scaling factor close to unity is needed for the correct behavior. Finally, a dynamical magnetic resonance property, the experimental nuclear spin-lattice relaxation rate R1 of monoatomic Xe gas due to the chemical shift anisotropy (CSA) mechanism is validated from first principles. This approach is based on molecular dynamics simulations over a large range of temperatures and densities, combined with the pairwise additive approximation for the shielding tensor. Therein, the shielding time correlation function is seen to reflect the characteristic time scales related to both interatomic collisions and cluster formation. For the first time, the physics of gaseous xenon is detailed in full in the context of CSA relaxation.

京都大学 / 0048 / 新制・課程博士 / 博士(理学) / 甲第21614号 / 理博第4521号 / 新制||理||1649(附属図書館) / 京都大学大学院理学研究科生物科学専攻 / (主査)教授 村山 美穂, 教授 幸島 司郎, 教授 平田 聡 / 学位規則第4条第1項該当 / Doctor of Science / Kyoto University / DGAM

Multiple antenna techniques are used to increase the robustness and performance of wireless networks. Multiple antenna techniques can achieve diversity and increase bandwidth efficiency when specially designed channel codes are used at the scheme’s transmitter. These channel codes can be designed in the space, time and frequency domain. These specially designed channel codes in the space and time domain are actually designed for flat fading channels and in frequency selective fading channel, their performance may be degraded. To counteract this possible performance degradation in frequency selective fading channel, two main approaches can be applied to mitigate the effect of the symbol interference due to the frequency selective fading channel. These approaches are multichannel equalisation and orthogonal frequency division multiplexing (OFDM). In this thesis, a multichannel equalisation technique and OFDM were applied to channel codes specially designed for multiple antenna systems. An optimum receiver was proposed for super-orthogonal space-time trellis codes in a multichannel equalised frequency selective environment. Although the proposed receiver had increased complexity, the diversity order is still the same as compared to the code in a flat fading channel. To take advantage of the multipath diversity possible in a frequency selective fading channel, super-orthogonal block codes were employed in an OFDM environment. A new kind of super-orthogonal block code was proposed in this thesis. Super-orthogonal space-frequency trellis-coded OFDM was proposed to take advantage of not only the possible multipath diversity but also the spatial diversity for coded OFDM schemes. Based on simulation results in this thesis, the proposed coded OFDM scheme performs better than all other coded OFDM schemes (i.e. space time trellis-coded OFDM, space-time block coded OFDM, space-frequency block coded OFDM and super-orthogonal space-time trellis-coded OFDM). A simplified channel estimation algorithm was proposed for two of the coded OFDM schemes, which form a broad-based classification of coded OFDM schemes, i.e. trelliscoded schemes and block-coded schemes. Finally in this thesis performance analysis using the Gauss Chebychev quadrature technique as a way of validating simulation results was done for super-orthogonal block coded OFDM schemes when channel state information is known and when it is estimated. The results obtained show that results obtained via simulation and analysis are asymptotic and therefore the proposed analysis technique can be use to obtain error rate values for different SNR region instead of time consuming simulation. / Thesis (PhD)--University of Pretoria, 2012. / Electrical, Electronic and Computer Engineering / unrestricted

Effective software performance testing is essential to the development and delivery of quality software products. Many software testing investigations have reported software performance testing improvements, but few have quantitatively validated measurable software testing performance improvements across an aggregate of studies. This study addressed that gap by conducting a meta-analysis to assess the relationship between applying Design of Experiments (DOE) techniques in the software testing process and the reported software performance testing improvements. Software performance testing theories and DOE techniques composed the theoretical framework for this study. Software testing studies (n = 96) were analyzed, where half had DOE techniques applied and the other half did not. Five research hypotheses were tested, where findings were measured in (a) the number of detected defects, (b) the rate of defect detection, (c) the phase in which the defect was detected, (d) the total number of hours it took to complete the testing, and (e) an overall hypothesis which included all measurements for all findings. The data were analyzed by first computing standard difference in means effect sizes, then through the Z test, the Q test, and the t test in statistical comparisons. Results of the meta-analysis showed that applying DOE techniques in the software testing process improved software performance testing (p < 05). These results have social implications for the software testing industry and software testing professionals, providing another empirically-validated testing methodology. Software organizations can use this methodology to differentiate their software testing process, to create more quality products, and to benefit the consumer and society in general.

Les données de classement, c.à. d. des listes ordonnées d'objets, apparaissent naturellement dans une grande variété de situations, notamment lorsque les données proviennent d’activités humaines (bulletins de vote d'élections, enquêtes d'opinion, résultats de compétitions) ou dans des applications modernes du traitement de données (moteurs de recherche, systèmes de recommendation). La conception d'algorithmes d'apprentissage automatique, adaptés à ces données, est donc cruciale. Cependant, en raison de l’absence de structure vectorielle de l’espace des classements et de sa cardinalité explosive lorsque le nombre d'objets augmente, la plupart des méthodes classiques issues des statistiques et de l’analyse multivariée ne peuvent être appliquées directement. Par conséquent, la grande majorité de la littérature repose sur des modèles paramétriques. Dans cette thèse, nous proposons une théorie et des méthodes non paramétriques pour traiter les données de classement. Notre analyse repose fortement sur deux astuces principales. La première est l’utilisation poussée de la distance du tau de Kendall, qui décompose les classements en comparaisons par paires. Cela nous permet d'analyser les distributions sur les classements à travers leurs marginales par paires et à travers une hypothèse spécifique appelée transitivité, qui empêche les cycles dans les préférences de se produire. La seconde est l'utilisation des fonctions de représentation adaptées aux données de classements, envoyant ces dernières dans un espace vectoriel. Trois problèmes différents, non supervisés et supervisés, ont été abordés dans ce contexte: l'agrégation de classement, la réduction de dimensionnalité et la prévision de classements avec variables explicatives.La première partie de cette thèse se concentre sur le problème de l'agrégation de classements, dont l'objectif est de résumer un ensemble de données de classement par un classement consensus. Parmi les méthodes existantes pour ce problème, la méthode d'agrégation de Kemeny se démarque. Ses solutions vérifient de nombreuses propriétés souhaitables, mais peuvent être NP-difficiles à calculer. Dans cette thèse, nous avons étudié la complexité de ce problème de deux manières. Premièrement, nous avons proposé une méthode pour borner la distance du tau de Kendall entre tout candidat pour le consensus (généralement le résultat d'une procédure efficace) et un consensus de Kemeny, sur tout ensemble de données. Nous avons ensuite inscrit le problème d'agrégation de classements dans un cadre statistique rigoureux en le reformulant en termes de distributions sur les classements, et en évaluant la capacité de généralisation de consensus de Kemeny empiriques.La deuxième partie de cette théorie est consacrée à des problèmes d'apprentissage automatique, qui se révèlent être étroitement liés à l'agrégation de classement. Le premier est la réduction de la dimensionnalité pour les données de classement, pour lequel nous proposons une approche de transport optimal, pour approximer une distribution sur les classements par une distribution montrant un certain type de parcimonie. Le second est le problème de la prévision des classements avec variables explicatives, pour lesquelles nous avons étudié plusieurs méthodes. Notre première proposition est d’adapter des méthodes constantes par morceaux à ce problème, qui partitionnent l'espace des variables explicatives en régions et assignent à chaque région un label (un consensus). Notre deuxième proposition est une approche de prédiction structurée, reposant sur des fonctions de représentations, aux avantages théoriques et computationnels, pour les données de classements. / Ranking data, i.e., ordered list of items, naturally appears in a wide variety of situations, especially when the data comes from human activities (ballots in political elections, survey answers, competition results) or in modern applications of data processing (search engines, recommendation systems). The design of machine-learning algorithms, tailored for these data, is thus crucial. However, due to the absence of any vectorial structure of the space of rankings, and its explosive cardinality when the number of items increases, most of the classical methods from statistics and multivariate analysis cannot be applied in a direct manner. Hence, a vast majority of the literature rely on parametric models. In this thesis, we propose a non-parametric theory and methods for ranking data. Our analysis heavily relies on two main tricks. The first one is the extensive use of the Kendall’s tau distance, which decomposes rankings into pairwise comparisons. This enables us to analyze distributions over rankings through their pairwise marginals and through a specific assumption called transitivity, which prevents cycles in the preferences from happening. The second one is the extensive use of embeddings tailored to ranking data, mapping rankings to a vector space. Three different problems, unsupervised and supervised, have been addressed in this context: ranking aggregation, dimensionality reduction and predicting rankings with features.The first part of this thesis focuses on the ranking aggregation problem, where the goal is to summarize a dataset of rankings by a consensus ranking. Among the many ways to state this problem stands out the Kemeny aggregation method, whose solutions have been shown to satisfy many desirable properties, but can be NP-hard to compute. In this work, we have investigated the hardness of this problem in two ways. Firstly, we proposed a method to upper bound the Kendall’s tau distance between any consensus candidate (typically the output of a tractable procedure) and a Kemeny consensus, on any dataset. Then, we have casted the ranking aggregation problem in a rigorous statistical framework, reformulating it in terms of ranking distributions, and assessed the generalization ability of empirical Kemeny consensus.The second part of this thesis is dedicated to machine learning problems which are shown to be closely related to ranking aggregation. The first one is dimensionality reduction for ranking data, for which we propose a mass-transportation approach to approximate any distribution on rankings by a distribution exhibiting a specific type of sparsity. The second one is the problem of predicting rankings with features, for which we investigated several methods. Our first proposal is to adapt piecewise constant methods to this problem, partitioning the feature space into regions and locally assigning as final label (a consensus ranking) to each region. Our second proposal is a structured prediction approach, relying on embedding maps for ranking data enjoying theoretical and computational advantages.