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构建列车和测试集的新方法用于评估与软件错误相关的机器学习模型的分配
Lukasz Chmielowski1,2, Michal Kucharzak3,4, Robert Burduk4
1Nokia Solutions and Networks sp. z o.o., 02-685, Warsaw, Poland. lukasz.chmielowski@nokia.com.
Scientific reports
|December 6, 2023
概括
本研究引入了一种用于评估软件开发中的机器学习模型的新方法,特别是用于错误追踪系统. 它通过结合时间依赖性来解决当前指标的局限性,从而导致更有效的模型评估.
科学领域:
- 软件工程 软件工程 软件工程
- 机器学习评估 机器学习评估
背景情况:
- 机器学习在软件开发工具中越来越多地用于诸如处理错误报告等任务.
- 这些模型的当前评估指标往往忽略了关键的时间依赖.
- 这种监督可能会影响模型性能评估的可靠性,特别是对于错误追踪系统.
研究的目的:
- 在软件开发环境中识别现有的机器学习模型评估指标的弱点,特别是对于错误报告.
- 提出和验证一个新的评估方法,考虑到时间依赖性.
- 为解决软件开发应用程序的机器学习中最先进方法的局限性.
主要方法:
- 对基本机器学习模型评估方法的全面审查.
- 确定当前指标的局限性,重点关注数据集分割中不考虑时间方面的问题.
- 开发和实施一种新的评估方法,包括时间依赖.
主要成果:
- 实验研究表明了新型评估方法的有效性.
- 与现有的最先进的方法相比,获得的结果明显不同.
- 拟议的方法通过考虑时间依赖,克服了当前指标的缺点.
结论:
- 这种新的方法可以更准确地评估软件开发中的机器学习模型,特别是用于错误跟踪.
- 纳入时间依赖对于可靠的绩效评估至关重要.
- 这项研究为软件工程中的机器学习应用程序提供了更强大的评估框架.
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