Scholarlink

Python is a high-level programming language widely used for a variety of applications such as web development, data analysis, artificial intelligence, the use of Genetic Algorithms and machine learning in defect prediction offers a promising approach to improving software quality and reliability. By maximizing the potential of these methodologies, software engineers can take proactive measures to tackle defects in their programs and construct software systems that are more robust and efficient. The prediction of the outcome is made using the GADL and GAEL models in our system. The accuracy of the GADL model is 0.72, with an ideal threshold of 0.0045. The accuracy of the GAEL model is 0.70, with an ideal threshold of 0.0042. The aggregate result of both models yielded an accuracy of 0.70, with an ideal threshold of 0.0025. The entire procedure, which encompasses training models and genetic algorithm iterations spanning 2 generations, requires around 264 s to complete the training process. The ensemble model’s capacity to process input and optimize its parameters in just over 31 s exhibits its efficiency within an acceptable time window.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Defect Prediction of Python Program Using Genetic Algorithm and Machine-Learning Techniques

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Rahul Kapse,
  • Bharati Harsoor

摘要

Python is a high-level programming language widely used for a variety of applications such as web development, data analysis, artificial intelligence, the use of Genetic Algorithms and machine learning in defect prediction offers a promising approach to improving software quality and reliability. By maximizing the potential of these methodologies, software engineers can take proactive measures to tackle defects in their programs and construct software systems that are more robust and efficient. The prediction of the outcome is made using the GADL and GAEL models in our system. The accuracy of the GADL model is 0.72, with an ideal threshold of 0.0045. The accuracy of the GAEL model is 0.70, with an ideal threshold of 0.0042. The aggregate result of both models yielded an accuracy of 0.70, with an ideal threshold of 0.0025. The entire procedure, which encompasses training models and genetic algorithm iterations spanning 2 generations, requires around 264 s to complete the training process. The ensemble model’s capacity to process input and optimize its parameters in just over 31 s exhibits its efficiency within an acceptable time window.