Towards an Early Software Estimation Using Log-Linear Regression and a Multilayer Perceptron Model

Software estimation is a tedious and daunting task in project management and software development. Software estimators are notorious in predicting software effort and they have been struggling in the past decades to provide new models to enhance software estimation. The most critical and crucial part of software estimation is when estimation is required in the early stages of the software life cycle where the problem to be solved has not yet been completely revealed. This paper presents a novel log-linear regression model based on the use case point model (UCP) to calculate the software effort based on use case diagrams. A fuzzy logic approach is used to calibrate the productivity factor in the regression model. Moreover, a multilayer perceptron (MLP) neural network model was developed to predict software effortbased on the software size and team productivity. Experiments show that the proposed approach outperforms the original UCP model. Furthermore, a comparison between the MLP and log-linear regression models was conducted based on the size of the projects. Results demonstrate that the MLP model can surpass the regression model when small projects are used, but the log-linear regression model gives better results when estimating larger projects

Enhancing Use Case Points Estimation Method Using Soft Computing Techniques

Software estimation is a crucial task in software engineering. Software estimation encompasses cost, effort, schedule, and size. The importance of software estimation becomes critical in the early stages of the software life cycle when the details of software have not been revealed yet. Several commercial and non-commercial tools exist to estimate software in the early stages. Most software effort estimation methods require software size as one of the important metric inputs and consequently, software size estimation in the early stages becomes essential. One of the approaches that has been used for about two decades in the early size and effort estimation is called use case points. Use case points method relies on the use case diagram to estimate the size and effort of software projects. Although the use case points method has been widely used, it has some limitations that might adversely affect the accuracy of estimation. This paper presents some techniques using fuzzy logic and neural networks to improve the accuracy of the use case points method. Results showed that an improvement up to 22% can be obtained using the proposed approach

Estimating the Costs of Foundational Public Health Capabilities: A Recommended Methodology

The Institute of Medicine’s 2012 report on public health financing recommended the convening of expert panels to identify the components and costs of a “minimum package of public health services” that should be available in every U.S. community. The report recommended that this minimum package include a core set of public health programs that target specific, high-priority preventable health problems and risks, along with a set of “foundational public health capabilities” that are deemed necessary to support the successful implementation of public health programs and policies. In response to this recommendation, the Robert Wood Johnson Foundation, in collaboration with the US Centers for Disease Control and Prevention and other national professional associations, formed the Public Health Leadership Forum, an expert consensus panel process to identify a recommended set of core programs and foundational capabilities for the nation. The Forum’s initial charge focused on the specification of foundational public health capabilities. The Foundational Capabilities Workgroup was formed as a part of the Forum to identify and define the elements to be included as foundational capabilities for governmental public health agencies at both state and local levels. The Robert Wood Johnson Foundation asked the National Coordinating Center for Public Health Services and Systems Research based at the University of Kentucky to convene a second expert panel workgroup, the Workgroup on Public Health Cost Estimation, to develop a methodology for estimating the resources required to develop and maintain foundational capabilities by governmental public health agencies at both state and local levels. Working in parallel with the Foundational Capabilities Workgroup, this Cost Estimation Workgroup has considered relevant cost-accounting models and cost estimation methodologies, and reviewed related cost estimation studies, in order to make recommendations on an approach for generating first-generation estimates of the costs associated with developing and maintaining foundational capabilities

Optimal R-Estimation of a Spherical Location

In this paper, we provide $R$-estimators of the location of a rotationally symmetric distribution on the unit sphere of $\R^k$. In order to do so we first prove the local asymptotic normality property of a sequence of rotationally symmetric models; this is a non standard result due to the curved nature of the unit sphere. We then construct our estimators by adapting the Le Cam one-step methodology to spherical statistics and ranks. We show that they are asymptotically normal under any rotationally symmetric distribution and achieve the efficiency bound under a specific density. Their small sample behavior is studied via a Monte Carlo simulation and our methodology is illustrated on geological data.Comment: Accepted in Statistica Sinic

Global, regional, and national mortality trends in older children and young adolescents (5–14 years) from 1990 to 2016: an analysis of empirical data

Summary Background From 1990 to 2016, the mortality of children younger than 5 years decreased by more than half, and there are plentiful data regarding mortality in this age group through which we can track global progress in reducing the under-5 mortality rate. By contrast, little is known on how the mortality risk among older children (5–9 years) and young adolescents (10–14 years) has changed in this time. We aimed to estimate levels and trends in mortality of children aged 5–14 years in 195 countries from 1990 to 2016. Methods In this analysis of empirical data, we expanded the United Nations Inter-agency Group for Child Mortality Estimation database containing data on children younger than 5 years with 5530 data points regarding children aged 5–14 years. Mortality rates from 1990 to 2016 were obtained from nationally representative birth histories, data on household deaths reported in population censuses, and nationwide systems of civil registration and vital statistics. These data were used in a Bayesian B-spline bias-reduction model to generate smoothed trends with 90% uncertainty intervals, to determine the probability of a child aged 5 years dying before reaching age 15 years. Findings Globally, the probability of a child dying between the ages 5 years and 15 years was 7·5 deaths (90% uncertainty interval 7·2–8·3) per 1000 children in 2016, which was less than a fifth of the risk of dying between birth and age 5 years, which was 41 deaths (39–44) per 1000 children. The mortality risk in children aged 5–14 years decreased by 51% (46–54) between 1990 and 2016, despite not being specifically targeted by health interventions. The annual number of deaths in this age group decreased from 1·7 million (1·7 million–1·8 million) to 1 million (0·9 million–1·1 million) in 1990–2016. In 1990–2000, mortality rates in children aged 5–14 years decreased faster than among children aged 0–4 years. However, since 2000, mortality rates in children younger than 5 years have decreased faster than mortality rates in children aged 5–14 years. The annual rate of reduction in mortality among children younger than 5 years has been 4·0% (3·6–4·3) since 2000, versus 2·7% (2·3–3·0) in children aged 5–14 years. Older children and young adolescents in sub-Saharan Africa are disproportionately more likely to die than those in other regions; 55% (51–58) of deaths of children of this age occur in sub-Saharan Africa, despite having only 21% of the global population of children aged 5–14 years. In 2016, 98% (98–99) of all deaths of children aged 5–14 years occurred in low-income and middle-income countries, and seven countries alone accounted for more than half of the total number of deaths of these children. Interpretation Increased efforts are required to accelerate reductions in mortality among older children and to ensure that they benefit from health policies and interventions as much as younger children. Funding UN Children\u27s Fund, Bill & Melinda Gates Foundation, United States Agency for International Development

Performance of Small Cluster Surveys and the Clustered LQAS Design to estimate Local-level Vaccination Coverage in Mali

<p>Abstract</p> <p>Background</p> <p>Estimation of vaccination coverage at the local level is essential to identify communities that may require additional support. Cluster surveys can be used in resource-poor settings, when population figures are inaccurate. To be feasible, cluster samples need to be small, without losing robustness of results. The clustered LQAS (CLQAS) approach has been proposed as an alternative, as smaller sample sizes are required.</p> <p>Methods</p> <p>We explored (i) the efficiency of cluster surveys of decreasing sample size through bootstrapping analysis and (ii) the performance of CLQAS under three alternative sampling plans to classify local VC, using data from a survey carried out in Mali after mass vaccination against meningococcal meningitis group A.</p> <p>Results</p> <p>VC estimates provided by a 10 × 15 cluster survey design were reasonably robust. We used them to classify health areas in three categories and guide mop-up activities: i) health areas not requiring supplemental activities; ii) health areas requiring additional vaccination; iii) health areas requiring further evaluation. As sample size decreased (from 10 × 15 to 10 × 3), standard error of VC and ICC estimates were increasingly unstable. Results of CLQAS simulations were not accurate for most health areas, with an overall risk of misclassification greater than 0.25 in one health area out of three. It was greater than 0.50 in one health area out of two under two of the three sampling plans.</p> <p>Conclusions</p> <p>Small sample cluster surveys (10 × 15) are acceptably robust for classification of VC at local level. We do not recommend the CLQAS method as currently formulated for evaluating vaccination programmes.</p