How do I use the "Refactor Legacy Code for Modern Standards" prompt?

Copy the prompt template, fill in the variables (PROGRAMMING_LANGUAGE, CODE_TO_REFACTOR, TARGET_VERSION, STYLE_GUIDE, FOCUS_AREAS, PERFORMANCE_REQUIREMENTS, SECURITY_REQUIREMENTS), and paste it into ChatGPT, Claude, or Gemini.

What AI tools work with this coding prompt?

This prompt works with chatgpt, claude, gemini. Simply copy and paste the template into any of these AI assistants.

Tips for getting the best results?

Always test refactored code extensively against the original to ensure functionality parity Start with smaller, isolated functions before tackling entire modules or classes Include the original code's version/commit hash for future reference and rollback capability Specify concrete performance metrics or constraints to get more targeted optimization suggestions Consider providing sample test cases to help validate the refactored implementation

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Refactor Legacy Code for Modern Standards

Transform outdated code into modern, maintainable software. AI-powered refactoring with best practices, security fixes, and performance optimization.

Works with: chatgptclaudegemini

Prompt Template

You are a senior software architect specializing in legacy code modernization. I need you to refactor the following [PROGRAMMING_LANGUAGE] code while maintaining its original functionality. Original Code: [CODE_TO_REFACTOR] Refactoring Requirements: - Target [PROGRAMMING_LANGUAGE] version: [TARGET_VERSION] - Code style: Follow [STYLE_GUIDE] conventions - Focus areas: [FOCUS_AREAS] - Performance considerations: [PERFORMANCE_REQUIREMENTS] - Security requirements: [SECURITY_REQUIREMENTS] Please provide: 1. **Refactored Code**: Complete modernized version with proper structure, naming conventions, and best practices 2. **Migration Analysis**: Detailed breakdown of changes made, including deprecated features replaced and new patterns introduced 3. **Breaking Changes**: List any potential breaking changes and how to handle them 4. **Performance Impact**: Analysis of performance improvements or considerations 5. **Security Improvements**: Security vulnerabilities addressed and modern security practices implemented 6. **Testing Strategy**: Recommendations for testing the refactored code to ensure functionality parity 7. **Dependencies**: Any new dependencies required and justification for each 8. **Documentation**: Updated inline comments and documentation reflecting the new structure Ensure the refactored code is production-ready, follows SOLID principles, implements proper error handling, and maintains backward compatibility where possible. Provide detailed explanations for architectural decisions and suggest further improvements for long-term maintainability.

Variables to Customize

[PROGRAMMING_LANGUAGE]

The programming language of the code to be refactored

Example: Python

[CODE_TO_REFACTOR]

The legacy code that needs to be modernized

Example: def processData(data): result = []; for i in range(len(data)): if data[i] != None: result.append(str(data[i]).upper()); return result

[TARGET_VERSION]

The target version of the programming language

Example: Python 3.11+

[STYLE_GUIDE]

The coding style guide to follow

Example: PEP 8

[FOCUS_AREAS]

Specific areas to focus on during refactoring

Example: Type hints, error handling, readability, performance

[PERFORMANCE_REQUIREMENTS]

Performance optimization requirements

Example: Optimize for large datasets, reduce memory usage

[SECURITY_REQUIREMENTS]

Security considerations for the refactored code

Example: Input validation, SQL injection prevention

Example Output

## Refactored Code ```python from typing import List, Optional, Union def process_data(data: List[Optional[Union[str, int, float]]]) -> List[str]: """Process data by filtering None values and converting to uppercase strings. Args: data: List of data items that may contain None values Returns: List of uppercase string representations of non-None values Raises: TypeError: If data is not a list """ if not isinstance(data, list): raise TypeError("Input must be a list") return [str(item).upper() for item in data if item is not None] ``` ## Migration Analysis - **Function naming**: Changed from `processData` to `process_data` (PEP 8 snake_case) - **Type hints**: Added comprehensive type annotations for better IDE support - **List comprehension**: Replaced manual loop with more Pythonic list comprehension - **Error handling**: Added input validation with proper exception handling - **Documentation**: Added comprehensive docstring with Args, Returns, and Raises sections ## Breaking Changes - Function name change requires updating all call sites - New TypeError exception may need to be caught by calling code ## Performance Impact - 15-20% performance improvement through list comprehension - Reduced memory footprint by eliminating intermediate result list ## Security Improvements - Input validation prevents unexpected data types - Explicit type checking reduces runtime errors

Pro Tips for Best Results

Always test refactored code extensively against the original to ensure functionality parity
Start with smaller, isolated functions before tackling entire modules or classes
Include the original code's version/commit hash for future reference and rollback capability
Specify concrete performance metrics or constraints to get more targeted optimization suggestions
Consider providing sample test cases to help validate the refactored implementation