基于智能学习的方法来确定敏捷实践中理想的团队规模.
Rodrigo Olivares1, Rene Noel1, Sebastián M Guzmán1
1Escuela de Ingeniería Informática, Universidad de Valparaíso, Valparaíso 2362905, Chile.
Biomimetics (Basel, Switzerland)
|May 24, 2024
概括
这项研究优化了敏捷的软件开发团队组织,使用生物灵感算法和集体学习. 该方法提高了沟通效率和团队规模,以更好地提供业务价值.
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
- 人工智能的人工智能
背景情况:
- 扩展敏捷软件开发在团队组织中提出了挑战,以实现有效的沟通和独立的价值交付.
- 优化团队结构以尽量减少沟通道 (团队内部和团队内部) 是一个关键问题.
研究的目的:
- 为优化敏捷软件开发团队组织提出和评估生物启发的算法.
- 通过整合合体学习来增强优化器性能.
主要方法:
- 应用一组生物启发的算法来建模团队组织作为一个优化问题.
- 整合集体学习以改善解决过程并实现近乎最佳的结果.
- 基于勘探和开采阶段对它们的融合特性进行元启发学分析.
主要成果:
- 拟议的方法为团队规模提供了更精确的定义,与行业标准保持一致.
- 合体学习提高了生物灵感优化器的性能.
- 这种新的方法与传统算法版本相比,显示出更高的性能.
结论:
- 生物启发的算法加上集体学习提供了优化敏捷团队结构的有效解决方案.
- 该方法解决了在规模化的敏捷环境中平衡沟通效率和独立团队能力的关键挑战.
- 这项研究提供了一种数据驱动的团队组建方法,可以提高整体软件开发的生产力.
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