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首页> 外文期刊>Applied Soft Computing >Predictive accuracy comparison between neural networks and statistical regression for development effort of software projects
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Predictive accuracy comparison between neural networks and statistical regression for development effort of software projects

机译：神经网络和统计回归之间的预测精度比较，以用于软件项目的开发工作

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To get a better prediction of costs, schedule, and the risks of a software project, it is necessary to have a more accurate prediction of its development effort. Among the main prediction techniques are those based on mathematical models, such as statistical regressions or machine learning (ML). The ML models applied to predicting the development effort have mainly based their conclusions on the following weaknesses: (1) using an accuracy criterion which leads to asymmetry, (2) applying a validation method that causes a conclusion instability by randomly selecting the samples for training and testing the models, (3) omitting the explanation of how the parameters for the neural networks were determined, (4) generating conclusions from models that were not trained and tested from mutually exclusive data sets, (5) omitting an analysis of the dependence, variance and normality of data for selecting the suitable statistical test for comparing the accuracies among models, and (6) reporting results without showing a statistically significant difference. In this study, these six issues are addressed when comparing the prediction accuracy of a radial Basis Function Neural Network (RBFNN) with that of a regression statistical (the model most frequently compared with ML models), to feedforward multilayer perceptron (MLP, the most commonly used in the effort prediction of software projects), and to general regression neural network (GRNN, a RBFNN variant). The hypothesis tested is the following: the accuracy of effort prediction for RBFNN is statistically better than the accuracy obtained from a simple linear regression (SLR), MLP and GRNN when adjusted function points data, obtained from software projects, is used as the independent variable. Samples obtained from the International Software Benchmarking Standards Group (ISBSG) Release 11 related to new and enhanced projects were used. The models were trained and tested from a leave-one-out cross-validation method. The criteria for evaluating the models were based on Absolute Residuals and by a Friedman statistical test. The results showed that there was a statistically significant difference in the accuracy among the four models for new projects, but not for enhanced projects. Regarding new projects, the accuracy for RBFNN was better than for a SLR at the 99% confidence level, whereas the MLP and GRNN were better than for a SLR at the 90% confidence level. (C) 2014 Elsevier B.V. All rights reserved.
机译：为了更好地预测软件项目的成本，进度和风险，有必要对其开发工作进行更准确的预测。主要的预测技术包括基于数学模型的预测技术，例如统计回归或机器学习（ML）。用于预测开发工作量的ML模型主要基于以下缺点得出结论：（1）使用导致不对称的准确性标准，（2）通过随机选择样本进行训练而导致结论不稳定性的验证方法和测试模型，（3）省略关于如何确定神经网络参数的解释，（4）从未经训练和测试的模型中得出互斥数据集的结论，（5）省略相关性分析，数据的方差和正态性以选择合适的统计检验以比较模型之间的准确性，以及（6）报告结果而未显示统计学上的显着差异。在这项研究中，将径向基函数神经网络（RBFNN）的预测精度与回归统计（模型与ML模型相比最频繁）的预测精度与前馈多层感知器（MLP）进行比较时，可以解决这六个问题通常用于软件项目的工作量预测）和通用回归神经网络（GRNN，一种RBFNN变体）。检验的假设如下：RBFNN的工作量预测准确性在统计学上优于从简单线性回归（SLR），MLP和GRNN获得的准确性（当从软件项目获得的调整后的功能点数据用作自变量时） 。使用了从国际软件基准标准组（ISBSG）版本11获得的与新项目和增强项目有关的样本。通过留一法交叉验证方法对模型进行了训练和测试。评估模型的标准基于绝对残差和弗里德曼统计检验。结果表明，对于新项目，四个模型之间的准确性存在统计上的显着差异，而对于增强型项目，则没有。对于新项目，在置信度为99％的情况下，RBFNN的准确性优于SLR，而在置信度为90％的情况下，MLP和GRNN则优于SLR。 （C）2014 Elsevier B.V.保留所有权利。

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