检测重构类型的软件提交消息基于集体机器学习算法
Dimah Al-Fraihat1, Yousef Sharrab2, Abdel-Rahman Al-Ghuwairi3
1Department of Software Engineering, Faculty of Information Technology, Isra University, Amman, 11622, Jordan. d.fraihat@iu.edu.jo.
Scientific reports
|September 12, 2024
概括
本研究引入了一种先进的机器学习方法,以准确地检测来自提交消息的软件重构类型. 使用XGBoost和TF-IDF的新方法实现了100%的准确性,显著改善了代码质量分析.
科学领域:
- 软件工程 软件工程 软件工程
- 机器学习 机器学习
- 自然语言处理自然语言处理.
背景情况:
- 重构增强软件设计,而不会改变外部行为.
- 提交消息对于跟踪代码更改至关重要.
- 从提交消息中分类重构类型具有挑战性,但对软件质量至关重要.
研究的目的:
- 为准确的重构类型检测提出一种全新的集体机器学习方法.
- 改进现有的分类重构文档分类方法.
主要方法:
- 使用了四个整体机器学习算法.
- 雇佣文本清理,预处理和功能工程 (TF-IDF,词袋).
- 应用超参数优化和二进制转换 (一对一,一对其余).
主要成果:
- TF-IDF特征工程技术的性能优于其他方法.
- 使用TF-IDF的XGBoost算法在所有指标上实现了100%的准确性.
- 结果超过了同一个数据集的当前最先进的性能.
结论:
- 拟议的整体机器学习方法有效地检测重构类型.
- 这种方法显著提高了软件的内部质量评估.
- 实现100%的准确性证明了高级机器学习在软件工程中的潜力.
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