test-driven-refactoring

To safely refactor untested code, establish characterization tests to document current behavior before making changes. This approach captures existing outputs as a baseline, enabling incremental refactoring with continuous verification to prevent regressions.

A golden master test records the current outputs of complex legacy code before refactoring. You need it when characterizing large, intricate systems where writing targeted unit tests is difficult, allowing you to verify incremental changes against saved baseline behavior.

Seam testing identifies specific locations in code where you can alter behavior without editing the logic itself, often through mocking. This technique reduces risk during migration by enabling incremental refactoring and continuous verification in isolated areas.

Yes, pytest can be used to implement test-driven refactoring on Python codebases lacking test coverage. The workflow involves writing characterization tests to capture current behavior, then executing incremental changes while continuously verifying outputs to prevent regressions.

The best way to incrementally refactor large codebases is a phase-based workflow: establish characterization tests, analyze coverage, execute incremental refactoring in small steps, and perform continuous verification. This reduces migration risk by validating behavior at each stage.

You need characterization tests before refactoring to document the actual current behavior of the system, not its intended design. This creates a safety net of regression testing that ensures your incremental changes preserve existing functionality throughout the workflow.