When code is poorly understood and its tests or documentation cannot be trusted, first record what it actually does for selected inputs. Check that your tests would fail if that behavior changed, then make one small, scoped edit and review what moved. Characterization tests preserve observed behavior—including bugs—so they are not proof that the behavior is correct.
What characterization tests establish
A characterization test captures an observable result of existing code: an output, side effect, or error for a particular input. It is useful when changing unfamiliar or risky code because it gives you a baseline before you edit. The point is not to endorse every behavior you find; it is to make that behavior visible so an unintended change is easier to catch.
Dakota Huang’s article, “Characterization Tests First, Then the Smallest Safe Change”, illustrates the approach with Python billing code. Its examples are one author’s demonstration, not a standardized recipe or evidence of a measured success rate. The process below keeps the useful distinction at its center: preserve behavior during refactoring, but change behavior deliberately when the task calls for a fix.
Turn the request into an observable behavior
Start by rewriting a vague ticket as a question a test can answer. Instead of “clean up billing,” specify what a selected input should produce, or which exception should occur and when. That gives the first test a narrow target and helps prevent an ostensibly small edit from becoming an unbounded redesign.
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Choose observations that matter to callers: returned values, errors, relevant side effects, and output shape. A test need not describe internal structure if the structure is not part of the behavior you need to protect.
Control inputs before capturing output
Work out which inputs can vary between runs and make them repeatable where practical. Huang’s Python example identifies the system date and a PLAN environment variable; it patches dependencies at the names used by the code under test. In Python, the correct patch target depends on how the dependency was imported, so this is an example-specific technique rather than a universal mocking rule.
- Control time when date-dependent behavior matters.
- Set environment variables explicitly instead of inheriting whatever happens to be in the test process.
- Replace network calls with controlled responses when the behavior under test does not require a live service.
- Fix random seeds or provide deterministic alternatives when random choices affect the result.
- Consider thread interleaving: if concurrency makes the observation unstable and cannot be controlled, narrow the test or defer the change.
If an important input cannot be reproduced, do not present a flaky observation as a reliable baseline. Reduce the scope to behavior you can execute and observe dependably.
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Capture representative results, then inspect them
Run the selected cases and record their actual outputs. A saved snapshot or “golden” file can make a large result easier to compare, but it is only a record of what happened in that run. Huang puts the caveat plainly: “A snapshot is not a truth claim.” Review the captured values before accepting them as expectations; an unnoticed defect can be frozen just as easily as legitimate behavior.
Use a few representative cases tied to real inputs and branches rather than snapshotting everything indiscriminately. Exact equality can also be too brittle for values such as floating-point results; choose a comparison that matches the behavior the task needs to preserve.
Pin important errors and branches separately
A broad output snapshot may not make a critical error path obvious. Add direct assertions for important branches, based on the code and its callers. Huang’s example checks an unknown-plan error, including a case where input rows are empty but the plan lookup still happens. That case is useful only if the same control flow matters in your own code; do not copy it as a universal edge case.
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For each behavior you decide to protect, make the assertion state the relevant outcome clearly. That makes intentional changes easier to distinguish from regressions and reduces the chance that a large snapshot diff hides a meaningful error change.
Check that the tests can detect a change
A passing test suite is not automatically a useful safety net. Try a deliberate, temporary mutation in a copy or otherwise isolated version of the code—for example, make a clamp for negative days behave incorrectly—and confirm that the relevant test fails. Restore the code after the check.
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Make the smallest change that serves the task
With a baseline you trust, change only what the task requires and rerun the relevant tests. A local rename or helper extraction may preserve behavior; a bug fix intentionally changes it. Huang’s example replaces a strict dictionary lookup with a fallback, which changes the observable result for an unknown plan, so the old error expectation must be replaced by an expectation for the intended new behavior.
Keep the unaffected observations in place. For a behavior-preserving refactor, selected inputs should still produce the same relevant outputs and errors. For an intentional behavior change, identify which expectation should move, update it to express the desired outcome, and retain tests for unrelated behavior.
Martin Fowler’s description of the second edition of Refactoring: Improving the Design of Existing Code (2018) emphasizes controlled, small steps: “By doing them in small steps you reduce the risk of introducing errors.” That principle applies to behavior-preserving refactoring. A bug fix is different: it changes behavior by design and needs an explicit expectation for the new result.
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When to stop or narrow the work
Characterization-first work is most valuable when behavior is unclear and a change could have unintended effects. It is not a ritual required for every edit. If the relevant code is already well specified and covered by trustworthy tests, a separate baseline exercise may add little. If unstable external inputs or concurrency prevent repeatable observations, reduce the scope to what can be characterized rather than claiming comprehensive protection.
The technique also cannot decide whether an observed behavior is correct. Use the task’s requirements or an explicit product decision to determine the intended outcome; then write or revise an assertion for that intent. Keep the characterization tests that protect behavior outside the deliberate change.
Further reading
For broader strategies on changing unfamiliar code, see Michael Feathers’s Working Effectively with Legacy Code. O’Reilly lists the book as published in September 2004 by Pearson, with ISBN 0131177052, and describes it as covering common legacy-code problems and tests that help ensure changes are not made unintentionally. It is relevant background reading, not the source of Huang’s specific workflow.
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