使用整体机器学习方案自动化软件错误严重性分类:一个真实的案例研究
Mohammadreza Namdar1, Farnaz Barzinpour1, Rassoul Noorossana1,2
1School of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.
PloS one
|October 15, 2025
概括
本研究介绍了一个自动化的软件错误严重性分类系统,使用集体学习和自然语言处理进行波斯语错误报告. 与手工方法相比,这种方法显著提高了准确性和速度,减少了开发时间和成本.
科学领域:
- 软件工程 软件工程 软件工程
- 人工智能的人工智能
- 自然语言处理自然语言处理.
背景情况:
- 手动软件错误报告分类是耗时和资源密集的.
- 现有的自动化方法往往侧重于结构化,英语数据集.
- 波斯语的独特语言特征需要专门的文本分类方法.
研究的目的:
- 开发和评估一个自动化系统来对软件错误严重程度进行分类.
- 为了解决在波斯语中错误报告分类的挑战.
- 为了提高错误分类过程的效率和准确性.
主要方法:
- 一种组合学习方法,结合了多类机器学习和文本分类技术.
- 应用自然语言处理 (NLP) 来分析非结构化的波斯语错误报告.
- 从一个案例研究中利用了4429个波斯虫报告的真实世界数据集.
主要成果:
- 开发的自动化方法在错误严重程度分类方面取得了高准确性.
- 与手动方法相比,该系统的分类时间明显更快.
- 拟议的方法有效地处理了波斯语言在文本分类方面的独特特点.
结论:
- 使用集体学习和NLP的自动错误严重性分类对波斯语是可行的和有效的.
- 该系统比手工分类提供了显著的改进,减少了开发开销.
- 这种方法可以减少软件开发时间和成本.
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