使用卷积神经网络和粒子群优化算法的软件成本估计预测
Moatasem M Draz1,2, Osama Emam3, Safaa M Azzam3
1Software Engineering Department, Faculty of Computers and Information, Kafrelsheikh University, Kafrelsheikh, Egypt. Moatasem.draz@fci.kfs.edu.eg.
Scientific reports
|June 7, 2024
概括
这项研究引入了一种新的深度学习模型,将卷积神经网络 (CNN) 和粒子群优化 (PSO) 结合起来,以准确地估计软件成本. 混合方法显著提高了预测准确性,并减少了参数调整中的手动工作.
科学领域:
- 计算机科学 计算机科学
- 软件工程 软件工程 软件工程
背景情况:
- 软件成本估计对于项目规划和资源分配至关重要.
- 现有的估计方法存在不准确性和不稳定性,需要先进的技术.
研究的目的:
- 开发和评估一种使用混合深度学习和机器学习技术进行软件成本估计的新型模型.
- 为了提高预测准确度,减少软件成本估计过程中的手动工作.
主要方法:
- 开发了一种混合模型,将卷积神经网络 (CNN) 集成为特征提取和粒子群集优化 (PSO) 进行超参数调整.
- 该模型在13个基准数据集上进行了训练和验证,采用时间序列预测原则.
- 使用平均绝对误差 (MAE),平均平方误差 (MSE),平均尺寸相对误差 (MMRE),根平均平方误差 (RMSE),中等尺寸相对误差 (MdMRE) 和预测精度 (PRED) 来评估性能.
主要成果:
- 拟议的CNN-PSO模型在所有13个数据集和6个评估指标中,与现有方法相比,表现优越.
- 该模型实现了更高的预测准确性和更好的稳定性和概括能力.
结论:
- 混合CNN-PSO模型为准确的软件成本估计提供了有希望和有效的解决方案.
- 这种方法显著减少了手工工作,并提高了软件成本预测的可靠性.
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