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Model to estimate the software development effort based on in-depth analysis of project attributes

机译：基于对项目属性的深入分析来估算软件开发工作量的模型

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Over the past years, numerous models have been proposed to estimate the development effort in the early stages of a software project. The existing models have mostly relied on soft computing techniques and weighting methods. Although they have reduced the complexity and vagueness of software project attributes, attempts are ongoing to develop more accurate and reliable estimation models. This paper is concentrated on selective classification of software projects based on underlying attributes to localise the development effort estimation process in a widely used model called analogy-based estimation (ABE). The proposed model is a combination of ABE, selective classification and a weighting system in which the attributes of different software projects are assigned different weights. In fact, the process of attribute weighting is customised based on the nature of project being estimated. A real data set was utilised to evaluate the performance of the proposed model. A comparison between the estimates achieved by the proposed model and those obtained from other well-known effort estimation models showed that the proposed model substantially improves the performance metrics. Along with the improvement of accuracy, the proposed model is able to be used in an extensive domain of software projects.
机译：在过去的几年中，已经提出了许多模型来估计软件项目早期的开发工作。现有模型主要依赖于软计算技术和加权方法。尽管它们降低了软件项目属性的复杂性和模糊性，但仍在尝试开发更准确和可靠的估算模型。本文集中在基于基础属性的软件项目的选择性分类上，以将开发工作量估计过程本地化为一种广泛使用的模型，该模型称为基于类比的估计（ABE）。所提出的模型是ABE，选择性分类和权重系统的组合，其中为不同软件项目的属性分配了不同的权重。实际上，属性加权的过程是根据要估算的项目的性质定制的。一个真实的数据集被用来评估所提出模型的性能。通过提议的模型获得的估计与从其他知名的工作量估计模型获得的估计之间的比较表明，提出的模型大大改善了性能指标。随着准确性的提高，提出的模型可以在软件项目的广泛领域中使用。

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