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The world is eagerly awaiting the development of machine learning technologies since there has been extensive research on the potential applications of machine learning for software defect identification. Examining publications that emphasize the bibliometric features of machine learning-based software defect detection is the goal of the study. The IEEE database was searched using a variety of keywords, yielding 219 papers about the subject between 2007 and 2024. Analysis revealed more publications in the subject domain between 2007 and 2024. Machine learning, convolutional neural networks, curriculum, and machine learning systems that co-occur with AI are some of the emerging themes. More international collaboration founded on common democratic values is required for the development of machine learning technologies for software defect detection.

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Software Defect Detection and Machine Learning: Bibliometric Approach

  • 章节

  • 发表日期：

    2025年

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  • John Philipose Villoth,
  • Saramma John Villoth,
  • Joseph Mani

摘要

The world is eagerly awaiting the development of machine learning technologies since there has been extensive research on the potential applications of machine learning for software defect identification. Examining publications that emphasize the bibliometric features of machine learning-based software defect detection is the goal of the study. The IEEE database was searched using a variety of keywords, yielding 219 papers about the subject between 2007 and 2024. Analysis revealed more publications in the subject domain between 2007 and 2024. Machine learning, convolutional neural networks, curriculum, and machine learning systems that co-occur with AI are some of the emerging themes. More international collaboration founded on common democratic values is required for the development of machine learning technologies for software defect detection.