Refactoring

C1 16+
  • Frequency of Use
    25 %
  • Retention Rate
    60 %
  • Complexity
    70 %
  • Refactoring Meanings

    noun the process of restructuring existing computer code without changing its external behavior

    Fields related to refactoring

    Software Development

    Refactoring is a key practice in software development where code is restructured to improve readability, maintainability, and performance.

    Software Engineering

    Refactoring is a fundamental concept in software engineering, helping developers to optimize their code and make it more robust and scalable.

    Agile Methodology

    Refactoring is an essential part of the Agile development process, allowing teams to continuously improve their codebase while delivering value to customers.

    Code Quality

    Refactoring is used to enhance code quality by eliminating code smells, reducing technical debt, and ensuring that the codebase remains clean and efficient.

    Continuous Integration/Continuous Deployment (CI/CD)

    Refactoring is integrated into CI/CD pipelines to ensure that code changes are continuously improved and tested before deployment.

    Occupation Usage of refactoring

    Software Developer

    Refactoring is commonly used in software development to improve the structure, readability, and maintainability of code without changing its functionality. Developers often refactor code to make it more efficient, reduce duplication, and adhere to best practices.

    Project Manager

    Project managers may use refactoring as a way to optimize project plans, schedules, and resources. By restructuring tasks or reallocating resources, project managers can improve project efficiency and outcomes.

    Quality Assurance Analyst

    Quality assurance analysts may use refactoring to enhance the testing process by improving test cases, scripts, and automation tools. Refactoring can help QA analysts identify and resolve defects more effectively.

    Data Scientist

    Data scientists may use refactoring to improve the performance and accuracy of machine learning models, data pipelines, and algorithms. By optimizing code and data structures, data scientists can enhance the reliability and scalability of their analyses.

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