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深度多度指标学习用于使用代码和过程指标预测移动应用程序缺陷.

Ahmed Abdu1, Hakim A Abdo2, Inam Ullah3

  • 1School of Information, Xi'an University of Finance and Economics, Xi'an, 710100, China.

Scientific reports
|November 4, 2025
PubMed
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此摘要是机器生成的。

这项研究引入了深度多度度指标学习模型 (DMLM) 以准确预测移动应用程序缺陷. DMLM有效地结合了代码和流程指标,在精力意识和非精力意识的场景中优于现有方法.

科学领域:

  • 软件工程 软件工程 软件工程
  • 机器学习 机器学习
  • 移动应用开发 移动应用开发

背景情况:

  • 准确的缺陷预测对于高效的移动应用程序开发和资源分配至关重要.
  • 现有的缺陷预测模型受限于它们依赖于孤立的数据源,无法捕捉代码和过程指标的相互作用.
  • 需要先进的模型,集成多种指标,以提高预测准确度.

研究的目的:

  • 为增强移动应用程序缺陷预测提出一个新的深度多度度指标学习模型 (DMLM).
  • 在一个统一的框架内,利用当前版本的代码指标和以前版本的过程指标.
  • 根据最先进的方法评估DMLM的性能.

主要方法:

  • 开发了一个深度多度指标学习模型 (DMLM),集成代码和过程指标.
  • 利用深度卷积神经网络 (CNN) 识别组合指标中的复杂模式.
  • 在来自 Git 存储库的 9 个真实世界 Android 应用程序上进行了实验.

主要成果:

  • 在非精力意识的设置中,DMLM显著超过了最先进的方法,显示出优越的曲线下面面积 (AUC),F1得分和马修斯相关系数 (MCC).
  • 在努力意识的场景中,DMLM模型还显示出与基线方法相比的优异性能 (PofB20).
关键词:
代码指标是指代码指标.深度神经网络是一个神经网络.移动应用程序缺陷预测预测过程指标 过程指标

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  • 这些发现证实了该模型在各种预测环境中的有效性.

    • 结论:

    • 拟议的DMLM通过整合代码和流程指标,有效地提高了移动应用程序缺陷预测.
    • 在移动应用程序开发中,DMLM提供了一个强大的解决方案,用于改善资源配置和软件质量.
    • 该研究强调了多度学习对于准确可靠的缺陷预测的重要性.