使用灰狼优化算法增强基于类比的软件成本估计
Taghi Javdani Gandomani1, Maedeh Dashti1, Sadegh Ansaripour2
1Department of Computer Science, Faculty of Mathematical Sciences, Shahrekord University, Shahrekord, Chaharmahal and Bakhtiari, Iran.
PeerJ. Computer science
|June 26, 2025
概括
这项研究通过将灰狼优化 (GWO) 算法与基于类比的估计 (ABE) 集成来增强软件成本估计 (SCE). GWO-ABE方法提高了预测项目成本的准确性和稳定性,为软件开发规划提供了更可靠的解决方案.
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:
- 准确的软件成本估计 (SCE) 对项目成功至关重要,但许多项目超过预算.
- 基于类比的估计 (ABE) 是一种流行的SCE方法,但它的准确性可以得到改进.
- 优化算法有可能提高ABE在软件大小和成本估计方面的精度.
研究的目的:
- 通过将灰狼优化 (GWO) 算法与ABE集成,为SCE提出一种创新的方法.
- 通过使用GWO优化其相似性函数来提高ABE的精度.
- 评估拟议的基于GWO的ABE (GWO-ABE) 方法用于软件成本预测的有效性.
主要方法:
- 数学建模并将GWO算法纳入ABE框架.
- 优化ABE相似性函数使用欧几里德和曼哈顿距离测量,具有特征权重.
- 使用交叉验证和与其他进化算法进行比较,对多个软件项目数据集进行GWO-ABE的严格评估.
主要成果:
- 与传统的ABE相比,GWO-ABE方法显示了更高的估计准确性和模型稳定性.
- 在关键性能指标方面,GWO-ABE取得了显著的改进,包括降低了相对错误的平均幅度 (MMRE) 和相对错误的中位数幅度 (MdMRE).
- 该方法的结果是更高的预测百分比 (PRED),表明在多个数据集中具有优异的预测性能,特别是在欧几里德距离函数中.
结论:
- 将GWO集成到ABE中有效地解决了通过优化特征权重来解决传统类比方法的局限性.
- 拟议的GWO-ABE方法为软件项目成本估计提供了可靠和优质的解决方案.
- 以GWO为例的Metaheuristic优化在推进软件估计技术,以更好地规划和管理项目方面发挥着重要作用.
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