利用软件工程中的众包技术开发网站

Muataz Abd Al-Mohsen, A. S. Abbas
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引用次数: 0

摘要

这项研究的意义在于众包工具,它支持软件工程任务,由不同的、身份不明的人群通过互联网发送想法和意见。众包全球在线工作调度软件工程任务,如需求提取、需求分析、设计、测试和编码,是需求工程的基本原则或思想。近年来,软件工程众包的影响越来越大,因为它对软件工程的多个方面产生了清晰可见的影响。在这项研究中,进行了一项搜索,以获得来自不同群体,年龄和专业的关于伊拉克重要和常用站点之一的众包意见,其中约有151人参与,通过回答(9)个单词“是”或“否”来表达他们的意见,因为这些问题的回答风格简单,简单和快速,目的是提取工程需求所需的有用信息。作为项目规划的依据。这些方法在论文中用于数据挖掘的概念,使用分类技术来发现未知的数据类别。这包括决策树、逻辑回归、随机森林分类器和k近邻算法,对每种方法进行评估,显示使用分类技术的最佳结果,使用98%决策树和随机森林分类器技术的最佳结果,而这已经足够了。处理相同的数据集。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Use Crowdsourcing in Software Engineering for The Development of The Website
The significance of the study lies in the crowdsourcing tool that supports software engineering tasks externally by different, unidentified groups of people who send ideas and opinions over the Internet. Software engineering Crowdsourcing global online work scheduling software engineering tasks such as requirements extraction, requirements analysis, design, testing, and coding is the basic principle or idea of requirements engineering. The impact of software engineering crowdsourcing has increased in recent years due to the clear and visible impact that has touched and worked on multiple aspects of software engineering. In this study, a search was conducted to obtain crowdsourcing opinions about one of the important and commonly used Iraqi sites from different groups, ages, and specialties, in which about (151) people participated to express their opinion by answering (9) questions with the word yes or no, because the questions are of an answering style Easy, simple and fast, for the purpose of extracting the required and useful information for engineering requirements, which serves as the basis for project planning. These methods are used in the thesis concept for data mining using classification techniques to discover unknown categories of data. This includes decision trees, Logistic Regression, random forests Classifier, and k-nearest neighbor algorithm, with an evaluation of each method showing the best result using classification techniques that were the best result using the 98% decision trees and random forests Classifier technique while is more than adequate. Work on the same data set.
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