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基于机器学习的测试检测气味检测测试.

Valeria Pontillo1,2, Dario Amoroso d'Aragona3, Fabiano Pecorelli1

  • 1Software Engineering (SeSa) Lab - University of Salerno, Fisciano, Italy.

Empirical software engineering
|March 8, 2024
PubMed
概括
此摘要是机器生成的。

机器学习显著改善了测试嗅觉检测的启发式方法,但性能仍然有限. 需要进一步的研究,以克服准确识别这些代码设计缺陷的挑战.

关键词:
经验软件工程是经验软件工程.机器学习 机器学习测试代码质量质量测试代码质量测试的味道和气味.

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科学领域:

  • 软件工程 软件工程 软件工程
  • 软件质量保证 软件质量保证
  • 机器学习应用 机器学习应用

背景情况:

  • 测试气味表明测试案例中的设计选择不足于最佳,对可维护性和有效性产生负面影响.
  • 现有基于启发式的自动化技术用于测试气味检测,但性能有限,依赖于可调节的值.

研究的目的:

  • 设计和评估一种基于机器学习 (ML) 的新方法来检测四种类型的测试气味.
  • 将ML模型的性能与最新的基于启发式检测技术进行比较.

主要方法:

  • 开发用于实验的测试气味最大的手动验证数据集.
  • 在项目内部和跨项目场景中对六个机器学习模型进行培训和评估.
  • 对基于ML的检测与现有的启发式方法进行比较分析.

主要成果:

  • 基于ML的方法表现出比启发式技术更好的性能.
  • 然而,没有一个ML模型实现了超过51%的平均F-Measure,这表明检测准确性有限.
  • 一项定性调查发现了当前阻碍有效测试气味检测的挑战.

结论:

  • 虽然机器学习提供了改进,但目前的方法在测试气味检测中难以达到高精度.
  • 解决已确定的挑战对于推进自动化测试气味检测领域至关重要.
  • 未来的研究应该专注于克服这些局限性,以加强软件质量保证.