利用机器学习算法来分配分布式敏捷软件开发中的任务
Dimah Al-Fraihat1, Yousef Sharrab2, Abdel-Rahman Al-Ghuwairi3
1Department of Software Engineering, Faculty of Information Technology, Isra University, 11622, Amman, Jordan.
Heliyon
|November 18, 2024
概括
机器学习在分布式敏捷软件开发 (DASD) 中有效地分配任务. 随机森林实现了96.7%的准确性,提高了效率并防止了项目失败.
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:
- 分布敏捷软件开发 (DASD) 在全球越来越多地被采用.
- 有效的任务分配对于减轻项目失败和客户不满等风险至关重要.
- 由于全球人才采购和降低成本,DASD中出现了协调和沟通方面的挑战.
研究的目的:
- 在DASD中应用机器学习 (ML) 预测算法,以实现最佳的任务对角色分配.
- 帮助软件管理人员提高任务分配的效率和有效性.
- 为应对DASD固有的协调和沟通挑战.
主要方法:
- 数据集预处理涉及清理,规范化和分成培训,验证和测试集.
- 评估了四个ML分类器:随机森林,决策树,K-最近邻居 (K-NN) 和AdaBoost.
- 绩效的评估是基于任务分配的预测准确性.
主要成果:
- 随机森林在任务分配预测方面表现出卓越的表现,准确率为96.7%.
- K-最近的邻居 (K-NN) 实现了94.2%的准确性.
- 决策树和AdaBoost显示了可比的结果,分别准确率为93.5%和93%.
结论:
- 机器学习模型在解决DASD环境中的任务分配复杂性方面非常有效.
- 该研究证实了ML在优化分布式软件项目的资源管理方面的潜力.
- 这些有希望的结果表明,在提高DASD项目成功率方面,ML的应用范围更广.
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