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This research paper explores the domain of code smells within Python programming, presenting methodologies for their mitigation through systematic refactoring strategies. Commencing with a literature review, the paper identifies prevalent code smells and delineates their associated refactoring techniques, establishing a foundational understanding. Subsequently, an in-depth analysis is conducted on a diverse dataset comprising Python code smells, facilitating the correlation of specific instances with optimal refactoring strategies. By integrating theoretical insights with empirical observations, this study contributes to the refinement of software engineering practices, offering a valuable resource for developers and stakeholders aiming to enhance code quality and maintainability in Python projects.

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Analysing Refactoring Strategy for Handling Code Smell for Python Codes

  • 会议论文

  • 发表日期：

    2025年

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  • Vishrut Aggarwal,
  • Akanksha Kushwaha,
  • Deepali Malhotra,
  • Rajni Sehgal Kaushik,
  • Deepti Mehrotra

摘要

This research paper explores the domain of code smells within Python programming, presenting methodologies for their mitigation through systematic refactoring strategies. Commencing with a literature review, the paper identifies prevalent code smells and delineates their associated refactoring techniques, establishing a foundational understanding. Subsequently, an in-depth analysis is conducted on a diverse dataset comprising Python code smells, facilitating the correlation of specific instances with optimal refactoring strategies. By integrating theoretical insights with empirical observations, this study contributes to the refinement of software engineering practices, offering a valuable resource for developers and stakeholders aiming to enhance code quality and maintainability in Python projects.