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• Statistical Inference 统计推断
• Statistical Computing 统计计算
• (Generalized) Linear Models 广义线性模型
• Statistical Machine Learning 统计机器学习
• Longitudinal Data Analysis 纵向数据分析
• Foundations of Data Science 数据科学基础

## 电子工程代写|软件项目作业代写Software Project代考|Software Cost/Time Estimation Techniques for GSD

The cost/time estimation has been in the focus of software engineering research for many decades, and hence a high number of different estimation techniques have been developed [38-40]. Unfortunately most of the techniques for software cost estimation have been developed before the recent trend on global software development. Many techniques assume that the software is developed locally, and therefore they do not take into account the additional challenges for the development of distributed software [41, 42].

Estimation for the development of distributed software differs from estimation of local software development at least in two different ways. Firstly, there is a large overhead effort caused by several factors such as language differences; cultural barriers, or time shifts between sites; etc. Secondly, many factors (such as the skills and experience of the workforce) are specific and cannot be considered globally for a project. In many projects, the development sites have very different characteristics, and thus the productivity and cost rate is different between sites.

In the recent research, techniques used to estimate project effort and task duration in distributed context [43] include expert judgment, estimation by analogy, and algorithmic models (i.e., COCOMO II, SLIM, and recently function point analysis-based models) [41].

## 电子工程代写|软件项目作业代写Software Project代考|Expert Judgment

Experts’ judgment is one of the methods by which assessors conduct their effort estimation via using their expertise and their logical reasoning to estimate the required amount of effort needed to develop a software product. The accuracy of this method mainly depends on the skills, knowledge, and experience of the assessors to estimate the required amount of effort to complete a given project. Expert judgment can be very accurate, but it fails to provide an objective and quantitative analysis of what are the factors that affect effort and duration in GSD context, and it is hard to separate real experience from the expert’s subjective view [44]. The accuracy of the estimates depends on how closely the project correlates with past experience and the ability of the expert to recall all the facets of historic projects.

Estimating by analogy means comparing the proposed project to previously completed similar project, where the project development information is known. Actual data from the completed projects are extrapolated to estimate the proposed project. This technique is relatively straightforward. Actually in some respects, it is a systematic form of expert judgment since experts often search for analogous situations so as to inform their opinion. The methodology that should be followed to succeed the estimations by analogy involves characterizing the proposed project, selecting the most similar completed projects whose characteristics have been stored in the historical data base, and deriving the estimate for the proposed project from the most similar completed projects by analogy [41, 45].

The algorithmic methods are designed to provide some mathematical equations to perform software estimation. These mathematical equations are based on research and historical data and resort to inputs such as source lines of code, number of functions to perform, and other cost/time drivers such as project effort, design methodology, task allocation, team size, etc. The algorithmic methods have been largely studied and offer several advantages such as generating repeatable estimations, refining and customizing formulas, supporting a family of estimations or a sensitivity analysis, and calibrating previous experience. Models such as COCOMO II (Constructive Cost Model) and SLIM Model are the most frequently algorithmic methods used in a GSD context [43]. In the following, we present:

# 软件项目代考

## 有限元方法代写

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## MATLAB代写

MATLAB 是一种用于技术计算的高性能语言。它将计算、可视化和编程集成在一个易于使用的环境中，其中问题和解决方案以熟悉的数学符号表示。典型用途包括：数学和计算算法开发建模、仿真和原型制作数据分析、探索和可视化科学和工程图形应用程序开发，包括图形用户界面构建MATLAB 是一个交互式系统，其基本数据元素是一个不需要维度的数组。这使您可以解决许多技术计算问题，尤其是那些具有矩阵和向量公式的问题，而只需用 C 或 Fortran 等标量非交互式语言编写程序所需的时间的一小部分。MATLAB 名称代表矩阵实验室。MATLAB 最初的编写目的是提供对由 LINPACK 和 EISPACK 项目开发的矩阵软件的轻松访问，这两个项目共同代表了矩阵计算软件的最新技术。MATLAB 经过多年的发展，得到了许多用户的投入。在大学环境中，它是数学、工程和科学入门和高级课程的标准教学工具。在工业领域，MATLAB 是高效研究、开发和分析的首选工具。MATLAB 具有一系列称为工具箱的特定于应用程序的解决方案。对于大多数 MATLAB 用户来说非常重要，工具箱允许您学习应用专业技术。工具箱是 MATLAB 函数（M 文件）的综合集合，可扩展 MATLAB 环境以解决特定类别的问题。可用工具箱的领域包括信号处理、控制系统、神经网络、模糊逻辑、小波、仿真等。

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