Test refactored code

  • 7 minutes

Code reviews and testing are integral to the refactoring process for large functions. Including code reviews and testing within the refactoring process helps guide refactoring decisions. The resulting process is validated at each step and ensures that functionality remains intact while improving code readability and maintainability.

Test strategy during refactoring

Testing drives the refactoring process, providing continuous validation as you break down large functions into smaller, focused methods. Each extraction should be immediately tested before proceeding to the next refactoring step.

Use GitHub Copilot for test generation

Use GitHub Copilot to create tests throughout the refactoring process:

Here are some examples prompts you can use to generate unit tests:

  • @workspace /tests Generate unit tests for the ValidateOrderItems method I'm about to extract
  • Create parameterized tests for CalculateDiscounts with edge cases
  • Generate test cases for all public methods in the refactored OrderProcessor class

Here are some examples prompts you can use to generate integration tests:

  • @workspace Generate integration tests for the refactored ProcessOrder method that verify all helper methods are called in the correct sequence
  • Create integration tests for the OrderProcessor class focusing on the interaction between ValidateOrder, CalculateTotal, and ApplyDiscounts methods
  • Generate tests that verify error handling flows correctly through the extracted validation methods

Regression testing approach

Code refactoring shouldn't change code behavior. To ensure consistency, you can implement continuous regression testing that validates outputs at each step of the refactoring process.

Consider the following approach:

  1. Capture baseline behavior: Before starting refactoring, record outputs for various inputs including edge cases, normal operations, and error conditions.

  2. Test each extraction: As you extract each method, immediately verify that outputs match the original implementation exactly.

  3. Use property-based testing: Continuously test invariants that must hold true regardless of the internal implementation details.

  4. Maintain test data sets: Keep comprehensive test data files covering all business scenarios to ensure consistent validation throughout refactoring.

Performance validation

If performance is a concern, monitor performance impact as you refactor large functions to ensure that improvements in maintainability don't sacrifice efficiency.

Note

Performance testing isn't always necessary during refactoring, especially if the changes are purely structural. However, if the original function is performance-critical, it's important to validate that refactoring doesn't introduce regressions.

Performance testing guidelines

Consider the following guidelines when validating performance:

  • Establish baseline metrics: Before refactoring, benchmark the original function's execution time and resource usage
  • Test after each extraction: Measure performance impact as you extract each method.
  • Memory profiling: Monitor memory allocation patterns during the refactoring process.
  • Load testing: Continuously verify performance under typical and peak loads.
  • Critical path analysis: Focus testing efforts on performance-sensitive operations that directly impact user experience.

Key metrics to monitor

Track these performance indicators throughout refactoring:

  • Execution time for common scenarios.
  • Memory allocation patterns.
  • CPU utilization under load.
  • Response time percentiles (P50, P90, P99).
  • Throughput for batch operations.

Test coverage during refactoring

Maintain comprehensive coverage as you extract and modify code:

Coverage targets

The following coverage targets help ensure that refactored code remains well-tested:

  • Line coverage: Maintain 80% or higher coverage as you create new methods.
  • Branch coverage: Test all conditional paths in both original and refactored code.
  • Edge cases: Include boundary conditions, null inputs, empty collections, and error scenarios.
  • Integration points: Verify all interactions between extracted methods as you create them.

Using GitHub Copilot for coverage analysis

Ask Copilot to identify gaps while refactoring:

  • @workspace What edge cases are not covered in the current test suite?
  • Suggest test cases for error handling in the methods I'm extracting
  • Identify untested code paths in the extracted helper functions
  • List all exception scenarios that should be tested

Common testing pitfalls during refactoring

Avoid these mistakes while testing throughout the refactoring process:

  • Testing implementation instead of behavior: Focus on what the code accomplishes, not the specific implementation details. Tests should remain valid as internal structure changes.

  • Ignoring integration points: Individual methods might work perfectly in isolation but fail when integrated. Test the complete workflow after each extraction.

  • Delaying performance validation: Measure performance impact immediately after each change to catch regressions early.

  • Insufficient error scenario testing: Verify error handling remains consistent with the original implementation, including exception types and error messages.

  • Overlooking side effects: Confirm that each refactoring step doesn't alter logging, database updates, or external system interactions.

Quality validation checklist

Use this checklist during each refactoring session to ensure quality:

  • ☐ Existing tests pass before starting refactoring.
  • ☐ Each extracted method has corresponding unit tests.
  • ☐ Integration tests verify correct interaction between components.
  • ☐ Performance benchmarks remain within acceptable ranges.
  • ☐ Code coverage meets or exceeds organizational targets.
  • ☐ Error scenarios behave identically to original code.
  • ☐ Documentation reflects current code structure.
  • ☐ Dependent systems continue to function correctly.
  • ☐ No new compiler warnings or code analysis issues.

Remember: Comprehensive testing during refactoring is an investment in code quality. It provides confidence that your improvements aren't introducing bugs while ensuring the refactored code is easier to maintain and extend. The time spent on continuous testing throughout the process pays dividends through reduced debugging and increased developer confidence.

Summary

Incorporating thorough testing and validation into the refactoring of large functions is essential for maintaining code quality. By using GitHub Copilot to assist with test generation and coverage analysis, developers can streamline the testing process while ensuring comprehensive validation. Continuous performance monitoring and adherence to a structured testing strategy help ensure that refactoring efforts lead to more maintainable code without sacrificing functionality or efficiency. Following best practices and avoiding common pitfalls during testing results in a successful refactoring process that enhances both code quality and developer confidence.