在软件故障预测中评估分解的Halstead Metrics的有效性
1Capital University of Science & Technology, Islamabad, Pakistan.
分解Halstead指标显著改善了软件故障预测 (SFP). 这种方法提高了准确性,F测量和AUC,优于传统的Halstead指标,用于识别易出错的软件组件.
科学领域:
- 软件工程 软件工程 软件工程
- 机器学习应用 机器学习应用
- 软件质量保证 软件质量保证
背景情况:
- 软件故障是不可避免的,而详尽的测试是不可行的.
- 软件故障预测 (SFP) 通过识别易出错的模块来帮助测试.
- 机器学习 (ML) 是SFP的主导方法,利用各种代码指标.
研究的目的:
- 提出和评估分解的哈尔斯特德基准指标的故障预测能力.
- 将分解的Halstead指标与SFP中的原始Halstead指标的性能进行比较.
- 调查颗粒度度量分解对SFP精度的影响.
主要方法:
- 为Java语言创建了分解的Halstead基础指标 (运算符和操作数).
- 进行了两个实验:一个是使用原始的Halstead度量,另一个是使用分解的度量,以及McCabe,LoC和Halstead衍生度量.
- 在五个公共数据集上使用了六个ML分类器 (逻辑回归,天真贝叶斯,决策树,多层感知子,随机森林,支持向量机).
主要成果:
- 分解的Halstead指标显著改善了所有数据集的SFP性能.
- 精度从0.82增加到0.97,F测量从0.81增加到0.99,AUC从0.79增加到0.99.
- 增强的性能证明了分解指标的优越故障预测能力.
结论:
- 将Halstead基础指标分解为运算符和操作数,提高了它们在软件故障预测方面的有效性.
- 这种细分的方法提供了更精确的代码覆盖范围,从而大大提高了预测准确度.
- 这些发现表明,分解的Halstead指标是优化软件测试工作的宝贵工具.
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