Feedforward Neural Networks for Predicting the Duration of Maintained Software Projects

2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA) Pub Date : 2016-12-01 DOI:10.1109/ICMLA.2016.0093

C. López-Martín

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引用次数: 2

Abstract

Once a software project has been developed and delivered, any modification to it corresponds to maintenance. Software maintenance (SM) involves modifications to keep a software project usable in a changed or a changing environment, reactive modifications to correct discovered faults, and modifications to improve performance or maintainability. Since the duration of SM should be predicted, in this study, after a statistical analysis of projects maintained on several platforms and programming languages generations, data sets were selected for training and testing multilayer feedforward neural networks (i.e., multilayer perceptron, MLP). These data sets were obtained from the International Software Benchmarking Standards Group. Results based on Wilcoxon statistical tests show that prediction accuracy with the MLP is statistically better than that with the statistical regression models when software projects were maintained on (1) Mid Range platform and coded in programming languages of third generation, and (2) Multi platform and coded in programming languages of fourth generation.

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预测软件项目维护周期的前馈神经网络

一旦软件项目被开发并交付，对它的任何修改都对应于维护。软件维护(SM)包括修改以保持软件项目在变化或不断变化的环境中可用，修改以纠正发现的错误，以及修改以提高性能或可维护性。由于SM的持续时间是需要预测的，因此在本研究中，在对多个平台和编程语言世代维护的项目进行统计分析后，选择数据集进行多层前馈神经网络(即多层感知机，MLP)的训练和测试。这些数据集是从国际软件基准标准组获得的。基于Wilcoxon统计检验的结果表明，当软件项目在(1)Mid Range平台上以第三代编程语言进行维护，(2)Multi平台上以第四代编程语言进行编码时，MLP的预测精度在统计学上优于统计回归模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA)

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