Using grey relational analysis to predict software effort with small data sets

Qinbao Song, M. Shepperd, C. Mair
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引用次数: 65

Abstract

The inherent uncertainty of the software development process presents particular challenges for software effort prediction. We need to systematically address missing data values, feature subset selection and the continuous evolution of predictions as the project unfolds, and all of this in the context of data-starvation and noisy data. However, in this paper, we particularly focus on feature subset selection and effort prediction at an early stage of a project. We propose a novel approach of using grey relational analysis (GRA) of grey system theory (GST), which is a recently developed system engineering theory based on the uncertainty of small samples. In this work we address some of the theoretical challenges in applying GRA to feature subset selection and effort prediction, and then evaluate our approach on five publicly available industrial data sets using stepwise regression as a benchmark. The results are very encouraging in the sense of being comparable or better than other machine learning techniques and thus indicate that the method has considerable potential
使用灰色关联分析预测小数据集下的软件工作量
软件开发过程固有的不确定性对软件工作预测提出了特殊的挑战。随着项目的展开,我们需要系统地解决缺失的数据值、特征子集选择和预测的持续演变,所有这些都是在数据饥饿和噪声数据的背景下进行的。然而,在本文中,我们特别关注项目早期阶段的特征子集选择和工作量预测。灰色系统理论(GST)是最近发展起来的一种基于小样本不确定性的系统工程理论,我们提出了一种新的灰色关联分析方法。在这项工作中,我们解决了将GRA应用于特征子集选择和努力预测的一些理论挑战,然后使用逐步回归作为基准在五个公开可用的工业数据集上评估我们的方法。在与其他机器学习技术相媲美或更好的意义上,结果非常令人鼓舞,因此表明该方法具有相当大的潜力
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