Do these 3 things before closing this tab:
1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteBefore changing a Python function, map how callers use it, capture the behavior they rely on, and run the relevant tests while the code is still untouched. That gives you a baseline to compare against after the edit. It reduces avoidable regressions; it cannot prove a change is safe.
1. Find the function and its boundary
Start with the definition, its docstring, immediate callers, and tests that already exercise it. The function body shows what it does locally; callers show how the rest of the program depends on it. Search the project for the function name and for the module or object through which callers access it. Also note dependencies it calls and any state it reads or changes.
If you have a live Python object and want to inspect its source, Python’s inspect module provides inspect.getsource() and inspect.getsourcelines(). The documentation describes getsource() as returning “the text of the source code for an object.” Source retrieval is not universal: getsource() can raise OSError when Python cannot retrieve the source and TypeError for built-ins. Interactive definitions may also lack retrievable source. In those cases, inspect the project file directly.
Source inspection is an orientation aid, not a complete dependency map. It cannot by itself reveal every caller or runtime effect, so combine it with project search and tests.
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2. Record the behavior callers depend on
Before editing, write down the function’s observable behavior. Consider the cases that matter in this project:
- Ordinary and boundary inputs, plus invalid inputs that callers may supply.
- Return values and their types or structure.
- Exceptions: whether they are raised, and under what conditions.
- Mutations to arguments, objects, or shared state.
- Relevant calls to dependencies, including any externally visible effects.
Turn those expectations into assertions where tests are missing. Prefer checking results and effects that callers rely on over asserting internal implementation details that a legitimate refactor may change. No inspection or testing tool can infer this behavior list automatically; it comes from understanding the function’s contract and its use.
3. Establish a pre-edit test baseline
Use the project’s existing test framework and conventions first. Python’s unittest supports test cases and test discovery; many projects instead use pytest or another runner. Pytest can also execute existing unittest-based tests.
Run the narrow tests that cover the function or its callers before making changes. Record failures and relevant output: a pre-existing failure is different from a regression introduced by your edit. If no focused test exists, add one around the behavior you identified, then run it against the untouched implementation to confirm the baseline.
4. Isolate dependencies only when useful
Use a real dependency when it is inexpensive and deterministic. When a boundary is external, nondeterministic, or otherwise hard to control, substitute it within the test’s scope. Pytest’s monkeypatch fixture can temporarily change attributes, dictionary entries, environment variables, and paths; pytest undoes those modifications after the requesting test or fixture finishes.
With unittest.mock.patch, patch the name the function actually looks up, not automatically the module where the dependency was originally defined. As Python’s mock documentation explains: “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.” For example, if a module imports a dependency into its own namespace, patch that local name. Patching the dependency’s defining module may leave the function’s reference unchanged.
patch restores the target when its scope exits. Consider autospec to constrain a mock to the real object’s attributes and signatures. Avoid permissive creation of attributes that do not exist unless production code genuinely creates them dynamically: such mocks can let tests pass against an API the program does not have. Mocks help isolate a function, but can hide integration problems, so keep tests using real collaborators where practical and follow focused runs with broader ones.
5. Use coverage to locate unexercised code
Coverage.py records which code ran and can point to lines or branches that could have run but did not. Run it when an unvisited path might correspond to a meaningful input or behavior you have not tested.
Coverage is a map of execution, not a verdict on test quality. A line can run without any assertion checking its result, and a high percentage cannot establish that tests would catch an incorrect outcome. Treat uncovered code as a reason to investigate and add meaningful behavior assertions, not as a target percentage to pursue on its own.
6. Make the change and compare results
- Before editing: run the focused tests and note failures, outputs, and any relevant coverage gaps.
- Make the function change: keep the edit scoped enough that a changed result can be traced to the relevant behavior.
- After editing: rerun the focused tests, then the broader relevant suite. In pytest, options such as
-kselect tests by name and-xstops after the first failure; use the project’s normal command and options where possible. - Compare: investigate new failures and changed outputs against the pre-edit baseline. A focused pass gives quick feedback, while the broader run can catch interactions with callers that an isolated test misses.
A passing test run is evidence about the cases the suite checks, not proof that every caller or runtime path is safe. The practical goal is to make the remaining uncertainty visible: know what was exercised, what was asserted, and what still depends on untested behavior.
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