Web超媒体应用程序开发工作量估算技术的比较

E. Mendes, I. Watson, Chris Triggs, Nile Mosley, S. Counsell
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引用次数: 95

摘要

一些研究比较了不同类型技术的预测精度,重点是线性回归和逐步回归,以及基于案例的推理(CBR)。我们相信仅使用一种CBR技术可能会使结果产生偏差,因为还有其他类型的CBR技术也可以用于努力预测。本文有两个目的。第一个是比较三种CBR技术的预测精度,以估计开发Web超媒体应用程序的工作量。第二个目标是比较最佳CBR技术的预测精度,根据我们的发现,与三种常用的预测模型,即多元线性回归,逐步回归和回归树。在估计过程中使用了一个数据集,结果表明,不同的预测精度度量给出了不同的结果。MMRE和MdMRE对多元回归模型的预测精度较高,而箱形图对CBR的预测精度较高。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A comparison of development effort estimation techniques for Web hypermedia applications
Several studies have compared the prediction accuracy of different types of techniques with emphasis placed on linear and stepwise regressions, and case-based reasoning (CBR). We believe the use of only one type of CBR technique may bias the results, as there are others that can also be used for effort prediction. This paper has two objectives. The first is to compare the prediction accuracy of three CBR techniques to estimate the effort to develop Web hypermedia applications. The second objective is to compare the prediction accuracy of the best CBR technique, according to our findings, against three commonly used prediction models, namely multiple linear regression, stepwise regression and regression trees. One dataset was used in the estimation process and the results showed that different measures of prediction accuracy gave different results. MMRE and MdMRE showed better prediction accuracy for multiple regression models whereas box plots showed better accuracy for CBR.
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