Before changing a Python function, trace how callers use it, record its observable behavior, and run the relevant tests. Then make the smallest change you can and repeat the focused tests followed by the broader relevant suite. This workflow reduces avoidable regressions; it cannot prove a change is safe.
1. Find the function and understand its boundary
Start with the definition and docstring, then inspect the function’s immediate callers and any existing tests that exercise it. Callers show how the function is actually used; source code alone cannot reveal every runtime effect or dependency.
If you have a live Python object, inspect.getsource() returns source text when Python can retrieve it. inspect.getsourcelines() can provide the source lines and their starting line number. Source may be unavailable for built-ins or interactive definitions: inspect.getsource() can raise OSError when it cannot retrieve source and TypeError for built-ins. In those cases, inspect the project file or other available implementation directly. See the Python inspect reference.
Use source inspection to orient yourself, not as a substitute for tracing callers. Note what each caller passes in, what it does with the result, and whether it relies on a particular exception, mutation, or dependency call.
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2. Define the behavior callers depend on
Before editing, write down the important observable behavior. This is a checklist for your investigation, not something a tool will generate automatically:
- Normal inputs and expected return values.
- Boundary inputs, such as empty collections, minimum or maximum values, or missing optional arguments.
- Invalid inputs and the exceptions callers should expect.
- Changes to objects or other state, including mutations visible to the caller.
- Calls to dependencies that matter to the function’s contract, such as a save, send, or lookup operation.
Prefer assertions about outcomes over assertions about internal implementation details. A refactor may legitimately change how a result is computed while preserving what callers observe.
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3. Establish a pre-change test baseline
Begin with the project’s existing test conventions and the tests that target the function or its callers. Run the narrowest useful selection before editing and note any failures, so you can distinguish existing problems from changes you introduce.
Projects may use unittest, pytest, or another runner. Python’s unittest framework provides test cases and test discovery. If the project uses pytest, pytest can also run unittest-based test cases. Use the project’s usual command and configuration rather than introducing a new runner just for one change.
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4. Isolate dependencies only when it helps
Use the real dependency when it is inexpensive and deterministic. Substitute a dependency when it is external, unreliable, expensive, or difficult to control in a test. A mock can make a test faster and more focused, but it may hide integration problems if it replaces too much of the real path.
Use pytest’s monkeypatch for temporary changes
The pytest monkeypatch fixture can temporarily change attributes, dictionary entries, environment variables, or paths. Those changes are undone after the requesting test function or fixture finishes. It is useful for controlling a specific external effect without leaving the test environment altered. See the pytest monkeypatch reference.
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Patch the name the function looks up
If you use unittest.mock.patch, patch the name in the namespace where the code under test looks it up—not necessarily where the dependency was originally defined. For example, if a module imports a function into its own namespace, patching the original module may not affect the already-imported name. Python’s documentation states: “The basic principle is that you patch where an object is looked up, which is not necessarily the same place as where it is defined.” Python’s “Where to patch” guidance explains this rule.
A patch target is restored when its scope exits. Where appropriate, autospec can constrain the mock to the real object’s attributes and signatures. Avoid permissive creation of attributes that production code does not have: a test may otherwise pass against an API that is not real.
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5. Use coverage to spot gaps, not certify safety
Coverage.py records which code ran and can identify code that could have run but did not. That makes it a useful map when you suspect a line or branch lacks a test. It does not tell you whether a test asserted the right result: a line can execute while an incorrect return value, exception, or side effect goes unnoticed.
Investigate unexecuted code and add assertions for meaningful behavior paths. Do not treat a high coverage percentage as proof of correctness, or add tests solely to raise a number. See the Coverage.py documentation.
6. Make the change, then test in layers
- Make one focused change. Keeping the change small makes failures easier to trace.
- Rerun the focused tests. This gives fast feedback on the behavior you intended to change.
- Run the relevant broader suite. Wider tests can catch interactions with callers and surrounding code that an isolated test misses.
- Compare with the baseline. Check whether the return values, exceptions, mutations, and relevant dependency calls still match the behavior you intended to preserve. Investigate new failures rather than dismissing them because the focused test passes.
With pytest, options such as -k can select tests by expression, and options such as -x can stop after the first failure. Use these for quick feedback, then run the broader relevant tests. The pytest usage guide covers test selection and execution options.
Quick Recap
Choose the check that answers the risk
| Technique | What it helps answer | What it cannot establish alone |
|---|---|---|
| Read the function and trace callers | How the function is implemented and where its results or effects are used. | Every runtime effect or behavior across all inputs. |
| Focused tests | Whether the edited behavior still meets the assertions for the selected cases. | Whether other callers or integrations remain correct. |
| Broader relevant suite | Whether surrounding tests reveal interaction or wiring failures. | Correctness for cases the suite does not exercise or assert. |
| Mocks or monkeypatch | How the function behaves when a dependency is controlled or replaced. | Whether the real dependency is wired and behaves correctly in the complete path. |
| Coverage.py | Which code ran and where unexecuted code may deserve investigation. | Whether executed code was tested against meaningful expected outcomes. |
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