The Evolution of Complexity in Apple Darwin: A Common Coupling Point of View

Common coupling increases the interdependencies between software modules. It should be avoided if possible. In previous work, we presented two types of categorization of common coupling, one is for single-kernel-based software, one is for multi-kernel-based-software. In this paper, we analyze the relationships between these two types of categorization and apply them to study the evolution of the complexity of Apple Darwin. The same conclusion about Darwin’s evolution is drawn based on the two types of categorization of common coupling: From version XNU-517 to version XNU-792, Darwin has restructured to reduce the number of difficulty-inducing high category (level) global variables in order to reduce the system complexity. However, due to the definition-use dependencies, the complexity of Darwin induced by global variables has increased from version XNU-517 to version XNU-792. 1

Do Consumers Trust the National Inspection Exemption Brands? Evidence from Infant Formula in China

Consumers are often uncertain about product quality and have to rely on different information, either given or pursued, to assess quality. Developing countries may lack institutional and technical resources to rigorously monitor and enforce product quality standards and/or to implement market-based instruments where market failures are common. The information-based instruments on product quality may work well in these countries as they reduce information asymmetry between firms and consumers. This study investigates one particular information-based instrument, the National Inspection Exemption (NIE) system in China. China launched the National Inspection Exemption (NIE) System in various industries in 2000 to award firms who are in compliance with the quality standards, to inform consumers of product quality, and to lessen the pressure on regulatory monitoring and enforcement of product quality standards. Once a firm is granted the NIE title by China's National Administration of Quality Supervision, Inspection and Quarantine (AQSIQ), its products are exempted from quality inspections by all governmental agencies at different levels for three years; but it is obligated to report the product quality condition to the local AQSIQ office annually. The NIE titled firms are also allowed to include the title in the product label and to use the status in the advertisement campaign. Based on the theoretical framework, we establish the hypothesis that consumers are more willing to buy the product with the NIE title and the NIE title is likely to increase sales revenue when consumers lack of means to assess quality. The empirical application of China dairy industry supports the theoretical hypothesis. In particular, using the firm-level panel data, we find that the NIE title boots sales revenue and the impact is both statistically and economically significant based on the difference-in-difference estimate and the random-fixed effect estimations. Furthermore, using the survey data collected right after the 2008 China milk scandal regarding the brand choice of infant formula among 1,228 mothers with infants and young children, we find that consumers’ preference for the NIE title still present even the NIE titled firms are involved in a food scare event. The positive NIE preference is particularly strong among highly educated consumers and those who buy domestic brands.brand choice, food safety, product quality, national inspection exemption, quality standards, Food Consumption/Nutrition/Food Safety, Institutional and Behavioral Economics,

Numerical Solutions of Stochastic Differential Equations

In this dissertation, we consider the problem of simulation of stochastic differential equations driven by Brownian motions or the general Levy processes. There are two types of convergence for a numerical solution of a stochastic differential equation, the strong convergence and the weak convergence. We first introduce the strong convergence of the tamed Euler-Maruyama scheme under non-globally Lipschitz conditions, which allow the polynomial growth for the drift and diffusion coefficients. Then we prove a new weak convergence theorem given that the drift and diffusion coefficients of the stochastic differential equation are only twice continuously differentiable with bounded derivatives up to order 2 and the test function are third order continuously differentiable with all of its derivatives up to order 3 satisfying a polynomial growth condition. We also introduce the multilevel Monte Carlo method, which is efficient in reducing the total computational complexity of computing the expectation of a functional of the solution of a stochastic differential equation. This method combines the three sides of the simulation of stochastic differential equations: the strong convergence, the weak convergence and the Monte Carlo method. At last, a recent progress of the strong convergence of the numerical solutions of stochastic differential equations driven by Levy processes under non-globally Lipschitz conditions is also presented

Using Negative Binomial Regression Analysis to Predict Software Faults: A Study of Apache Ant

Negative binomial regression has been proposed as an approach to predicting fault-prone software modules. However, little work has been reported to study the strength, weakness, and applicability of this method. In this paper, we present a deep study to investigate the effectiveness of using negative binomial regression to predict fault-prone software modules under two different conditions, self-assessment and forward assessment. The performance of negative binomial regression model is also compared with another popular fault prediction model—binary logistic regression method. The study is performed on six versions of an open-source objected-oriented project, Apache Ant. The study shows (1) the performance of forward assessment is better than or at least as same as the performance of self-assessment; (2) in predicting fault-prone modules, negative binomial regression model could not outperform binary logistic regression model; and (3) negative binomial regression is effective in predicting multiple errors in one modul

Development Of Resonance Fluorescence Lidar For Studies Of The Aurora

Thesis (Ph.D.) University of Alaska Fairbanks, 2007In this thesis I present resonance fluorescence lidar studies of the middle and upper atmosphere. I focus on two specific applications; lidar measurements of heat fluxes in the mesosphere, and lidar measurements of auroral nitrogen ions in the thermosphere. In the heat flux study, I determine the limitations in state-of-the-art sodium Doppler wind-temperature lidar measurements. I conduct statistical analysis of current lidar measurements using analytical and Monte Carlo techniques and extend them to consider future measurements. I find that the expected biases for summertime flux measurements in polar regions will be larger than the geophysical values of the fluxes. In the nitrogen resonance lidar study, I conduct a simulation of the measurements under realistic auroral conditions and found that current lidar systems should be able to make statistically significant measurements of the nitrogen profile at a resolution of 6 km and 300 s. I develop a prototype nitrogen resonance lidar system operating at 390 nm. This lidar system is based on an existing dye laser-based iron resonance lidar system that operates at 372 nm. I designed and implemented a tuning control system that allows 1 pm resolution in the laser tuning. I made a set of field measurements and found that the performance of the prototype lidar was less than expected. I conduct an engineering analysis of the measurements and conclude that the lower than expected performance is due to the lasing characteristics of the dye laser