在敏捷软件开发中用于预测分析的智能技术
Sahana P Shankar1,2, Shilpa Shashikant Chaudhari3, Vinaytosh Mishra4,5
1Department of Computer Science and Engineering, M.S. Ramaiah Institute of Technology (Affiliated to Visvesvaraya Technological University, Belgaum), Bengaluru, Karnataka, 560054, India. sahanaprabhushankar@gmail.com.
Scientific reports
|February 25, 2026
概括
机器学习模型使用敏捷努力估计软件数据集预测软件问题解决时间. XGBoost在各种错误指标上表现出卓越的性能,提高了项目管理和资源配置.
科学领域:
- 软件工程 软件工程 软件工程
- 机器学习 机器学习
- 数据科学数据科学数据科学
背景情况:
- 软件开发的复杂性需要先进的项目管理工具.
- 预测分析用于解决问题的时间估计,改善决策和资源分配.
- 来自GitHub的敏捷努力估计软件 (AgES) 数据集为分析提供了丰富的功能.
研究的目的:
- 分析机器学习模型以预测软件问题解决时间.
- 用MAE,MSE,RMSE和MdAE等指标来评估模型性能.
- 确定解决问题的时间预测最有效的方法.
主要方法:
- 应用传统和先进的机器学习模型 (神经网络,随机森林,线性回归).
- 使用了AgES数据集,包括贡献者专业知识,问题类别和组件等功能.
- 使用平均绝对误差 (MAE),平均平方误差 (MSE),根平均平方误差 (RMSE) 和中位数绝对误差 (MdAE) 评估模型.
主要成果:
- XGBoost算法在被认为的错误指标中通常表现最好.
- 对比分析包括AgES数据集与现有的敏捷数据集 (TAWOS,Choet等. ) 的情况.
- 模型评估强调了对现实世界软件项目管理的实际影响.
结论:
- 机器学习为软件项目管理提供了强大的预测工具.
- 准确的问题解决时间预测可以实现更好的规划和资源管理.
- 该研究详细介绍了模型培训,特征的重要性以及ML在软件开发中的变革潜力.
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