基于SVC_RBF和神经网络反向传播的敏捷项目工作量估计优化方法

IF 1.1 Q3 INFORMATION SCIENCE & LIBRARY SCIENCE
Manju Vyas, N. Hemrajani
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引用次数: 0

摘要

摘要在当前的软件行业文化中,项目管理下的一项重要而关键的任务是准确估计导致项目成功完成的工作量。成功完成的项目意味着项目是在计划预算和时间表内开发的,这主要与准确的工作量估计有关,而对工作量和成本的不准确估计会导致项目在交付成本、时间和其他参数方面失败。大多数研究都是在使用传统框架评估软件项目时提出的。近年来,随着技术的进步和对技术变化的适应要求,敏捷开发方法论在软件行业的研究和开发中引起了极大的兴趣。敏捷的动态性质需要开发各种技术,这些技术可以用于软件成本和工作量估计。本文对几种流行的技术进行了研究,并提出了一种将两种技术相结合的新技术。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A novel approach for optimization of effort estimation of agile projects using SVC_RBF along with neural network backpropagation
Abstract In current scenario of software industry culture, an important and crucial task under project management is accurate estimation of effort which leads to successful project completion. A successfully completed project means that the project is developed within the planned budget and timeline which is mostly related to accurate effort estimation whereas inaccurate estimation of effort and cost results in failure of a project in context of delivery cost, time and other parameters. Most of the research has been proposed in the estimation of software projects using traditional frameworks. In recent years as there are technological advancements and there is a requirement of adaptation to technological changes, agile development methodology has attracted a lot of interest in research and development in software industries. The dynamic nature of agile requires development of various techniques which may be used for software cost and effort estimation. This paper deals with the study of few popular techniques and proposes a novel technique using ensemble of two techniques.
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来源期刊
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES INFORMATION SCIENCE & LIBRARY SCIENCE-
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21.40%
发文量
88
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