# Testing Complexity Claims > Designs and reviews tests for every complexity, behavioral, and performance claim in documentation. Use when adding or changing claims, auditing claim or API coverage, or creating module complexity tests. - Skill: `heikkitoivonen/testing-complexity-claims` (Agent Skill) - Install (CLI): `npx skillmds@latest add heikkitoivonen/testing-complexity-claims` - Raw SKILL.md: https://api.skillmd.com/api/skills/heikkitoivonen/testing-complexity-claims/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: Docs & Writing - Author: heikkitoivonen (https://skillmd.com/u/heikkitoivonen) - Updated: 2026-09-22 - Page: https://skillmd.com/skills/heikkitoivonen/testing-complexity-claims --- # Testing Complexity Claims Turn each documentation claim into executable evidence when execution can settle it, and explicitly account for the claims it cannot settle. ## Model the File on the csv Tests `tests/test_csv_complexity.py`, paired with `docs/stdlib/csv.md`, is the reference test file. Read it in full before writing or reviewing a module's tests, and shape the new file after it rather than after whichever test files were touched most recently: a file modelled on its neighbours inherits their departures from the reference and adds its own. The parts to reproduce: - **A module docstring that carries the evidence.** It opens "Tests for docs/stdlib/.md." and says in a paragraph how the page's claims are settled. A "Measurement scope:" list then gives the evidence per claim: for a timing or allocation measurement, the sizes used, the ratio or traced peak asserted, and where it matters the versions; for a direct behavioural check, what was observed; for a source-only bound, the released CPython file it follows from. A "Not settled here:" list closes it with the category C and D claims and the reason for each, and the dimensions the measurements held fixed. Everything a later reader needs to re-run or extend a measurement is there; nothing about what the page used to say is. - **`from __future__ import annotations`**, then a `PAGE` path and an `EXPECTED_BLOCKS` count as module constants. - **Small named helpers** for the measurements the file repeats: a fastest-of- N timer in nanoseconds, a traced-peak-allocation probe, a block extractor and a subprocess runner. - **One test class per table section or claim**, named for the behaviour it establishes (`TestReadingIsLazy`, `TestWritingHoldsOneRow`, `TestSnifferWalksTheWholeSample`), with a docstring that quotes or paraphrases the row it covers and says how the test separates the documented behaviour from the plausible wrong one. Test method names read as sentences: `test_building_a_reader_reads_nothing`. - **A `TestDocumentedExamples` class** that asserts the block count, runs each block that can be executed safely in its own subprocess and working directory, records the concrete reason for any block it excludes, reports failures by page and line, and mutation-tests its own runner by flipping one assertion in a block, asserting first that the mutation changed the source. The file carries no API coverage test of its own. Name coverage is the job of the page-scoped audit gate in *Inventory Before Testing*; run that, and do not add a `dir()`-based extractor to the test file. ## Choose a Durable Level of Abstraction Concentrate reviews and tests on Big-O characteristics that matter to a reader's choice: the growth class, its size variables, meaningful best/average/ worst distinctions, output size, callback cost, and bounded versus unbounded behavior. A finding should normally change one of those conclusions. Do not turn constant factors, benchmark ratios, incidental CPython steps, rare custom-protocol behavior, or minor wording in test comments into review findings unless they make the documented complexity materially misleading. Respect explicitly scoped bounds such as "after first access", "cache hit", "auxiliary space", or "excluding callback cost". Do not flag omitted out-of-scope work unless the page presents the bound as total or the omission materially changes a reader's decision. Use CPython source to establish the durable bound, not to reproduce a release's implementation in prose or tests. When caller-defined work can dominate, name it once as a variable such as callback cost or key cost rather than cataloguing pathological implementations. Inventories remain exhaustive so false claims are not silently blessed, but report and fix them at the highest useful level. Prefer one stable growth-class test over several microbenchmarks that pin mechanisms or constants likely to change between releases. ## Inventory Before Testing Read the page and list all claims, not only Big-O notation: - every Time, Space, and Notes table cell; - complexity annotations in code blocks; - prose about operation counts, copying, caching, laziness, allocation, short-circuiting, implementation, or relative cost; - version-specific behavior and API contracts; - performance recommendations and comparisons; - stated example output or exceptions. Map every inventory item to one test or to an explicit untestable rationale. Test names and docstrings are labels, not evidence: confirm each assertion actually distinguishes the documented behavior from a plausible wrong one. An inventory of existing claims cannot reveal omitted APIs. Use the shared page-scoped audit gate in *Coverage Is a Claim* in `documenting-complexity-modules` before reviewing and on the final page: ```bash uv run python scripts/audit_documentation.py --page docs/stdlib/os.md --check --include-review ``` Use the actual English page path. Resolve reported misses and account explicitly for unresolved/unavailable and unclassified APIs. Do not duplicate the audit with module-specific `dir()` coverage extractors. Zero misses establishes name coverage only: each added API still needs a justified bound and claim evidence. Report API completeness separately from correctness; passing claim tests alone does not establish a complete module review. A module belonging exclusively to another platform is not a coverage defect; an import that fails for any other reason still is. See *A Foreign Platform's Gap Is Not This Platform's Blocker* in `documenting-complexity-modules` for the three classes and what each one still has to report. ## Classify Each Claim ### A. Explanatory claim beyond the table Write a focused test. Prefer direct observation: - count `__eq__`, callback, comparison, iterator, filesystem, or protocol calls; - assert identity, mutation, allocation, laziness, cache reuse, output size, or exception behavior; - substitute a counting or recording object at the operation boundary; - compare state before and after the operation. Two probes settle most space and dimension claims on their own. **Check identity before you price a space bound.** An accessor that returns an existing object is O(1), however big that object is; one that builds a fresh copy is O(n). `vars(obj)`, `EnumClass.__members__`, `cursor.description` and `Row.keys()` all read as O(n) and are not. `a is b`, or a traced peak of zero, separates them with no tolerance - and the matching `.copy()` is the control that proves the O(1) is real rather than an unmeasured operation. **Vary shape at a fixed total to expose a hidden dimension.** When one variable is doing the work of two, build two inputs with the *same* total and different shape; a bound with only one variable predicts they cost the same. Equal-length query strings split into ten fields or a thousand differ by x27; equal node counts flat or nested differ by x2; directory trees of equal entry count differ by x8 deep against bushy; a one-character CSV row costs 923x more against a 10,000-field header than a 10-field one. Each of those gaps is a size variable the row was missing. Place broadly shared prose-claim tests in `tests/test_builtin_claims.py` or `tests/test_stdlib_claims.py`, organized by page/module. If the module already has a cohesive test file, keep its claims there. ### B. Restatement of the page's table Cover it in `tests/test__complexity.py`, shaped as *Model the File on the csv Tests* describes. Test all meaningful terms and cases in the row - not merely the happy path. For `O(k + B)`, vary `k` while holding `B` stable and vary `B` while holding `k` stable when practical. Check space claims with identity, mutation, output size, or allocation measurement as appropriate. ### C. Claim execution cannot settle Record the page and reason in the relevant test file's module docstring. Typical examples include network round trips without a real peer, backend-dependent costs, removed modules, and definitional or source-only facts. Cite released CPython source or official docs in the documentation where appropriate. Do not disguise category C as a skipped test, and never mark a wrong translation or unverified claim current merely to make checks pass. ### D. Claim only another platform can settle A row about `winreg`, `msvcrt`, `os.startfile` or a Unix-only signal call is testable - somewhere else. Guard it with a platform condition, in the same way a version-specific claim is guarded by `sys.version_info`: ```python @pytest.mark.skipif(sys.platform != "win32", reason="Windows-only API") ``` The guard is the point. A test that fails because it asserts another platform's behavior is a defect in the test, not evidence about the module, and leaving it red trains the next reader to ignore a red suite. A test whose claim your platform can reach must not be skipped to make the suite green. Two things follow for reporting. A skip here is not coverage: the claim is unverified on every run this project actually performs, so say so in the module docstring next to the category C entries rather than counting it as tested. And a suite that is green on Linux says nothing about the Windows-only rows in it - never describe such a page as fully verified without naming which rows only CI on another platform, or a reader on one, could confirm. ## Corrections Are New Claims A fix does not only remove a wrong claim, it writes a replacement — and that replacement arrives with none of the scrutiny the original just received. Inventory and classify it before publishing it, exactly as you did the text it replaces. Over a sustained review cycle most surviving defects are found in prose written by earlier fixes rather than in the original page. The measurement that motivated the fix usually covers one sentence. The explanation written around it goes in untested, and that is where corrections go wrong: - naming a mechanism the measurement did not observe ("sorts the input for a deterministic order", where the sorted path was measured and the fallback is not ordered at all); - asserting a lifetime or invariant in passing ("the cached entry lives as long as its key does", where an unrelated call clears the cache); - restating a bound for a path that was not measured, such as an immediate-failure cost presented as the cost of every failure; - naming one end of a range as though it were the whole, such as a worst case with no best case, or the reverse. Apply one rule to every sentence of a correction: if it claims something about cost, ordering, lifetime, allocation, or call counts, and no test distinguishes it from its negation, either test it or cut it. Prefer cutting to hedging, and a shorter true row to a longer one carrying a fresh untested clause. When a correction rests on a single measured input, record in the test docstring which dimensions were *not* varied — element cost, operand width, input order, arity, callback cost. A named untested axis can be checked later; an implied one reads as covered. ## Keep Measurements in the Test Settling a claim generates prose: the ratio you just measured, the code path you just read, a caveat about the one input shape you used. Almost none of it belongs on the page. The page carries the Big-O characteristic and the size variables it is expressed in; the numbers, the mechanism and the untested axes go in the test and its docstring, where they can be re-run and where the next CPython release fails them loudly instead of leaving a stale sentence behind. So "either test it or cut it" has a third outcome, and it is often the right one: keep the test, drop the sentence. A fact that needed a stopwatch to settle is usually a fact the page should state qualitatively — which side wins and why, not by how much — or not at all. Trimming a claim discharges it; a claim that is gone needs no test, and the inventory shrinks with the page. ## Use Timing Only When Necessary If direct observation cannot distinguish the growth class: 1. Measure before choosing sizes or thresholds. 2. Compare at least two input sizes and assert a ratio that separates the claimed shape from the excluded shape; avoid absolute nanosecond limits. 3. Choose inputs large enough that setup, timer resolution, and fixed overhead do not dominate. Move setup outside the timed operation. 4. Use repeated runs and the fastest sample where that matches local tests. 5. Pick the framing with the widest empirical gap, not the smallest example that happens to pass. 6. Mark the test `@pytest.mark.timing`. 7. Run it on the pinned interpreter, which is the newest supported version. CI covers the whole matrix, so reproduce the matrix locally only for a test whose claim is about another version, and then run just that version. Prefer one robust invariant over version branching: a behaviour can change in a middle release and change back, so the two boundaries settle nothing about the versions between them. 8. Include measured values in assertion failures so regressions are diagnosable. Those values stay in the test; never quote them on the page. Avoid microbenchmarks when a call counter, identity check, or state observation can prove the same fact without tolerance. ## Validate Examples and Test Strength - Execute every Python fenced block on an edited page. A generic extractor may compile each block with a filename containing the Markdown line number, then execute it in a fresh namespace. Account explicitly for examples requiring optional third-party packages or external services. - Assert that the page contains the expected number of examples so an extractor cannot silently test nothing. - Verify displayed output and claimed exceptions where they matter; successful execution alone does not validate comments. - When using source substitution, monkeypatching, or text mutation, first assert the substitution matched and changed the target. A mutation that never applied proves nothing. - Exercise representative input shapes. Ordered, random, duplicate-heavy, adversarial, shallow, and deep inputs can expose different paths. - Keep tests deterministic and restore global state, caches, warning filters, import paths, decimal contexts, and garbage-collector state. ## Review Against Sources Use a released CPython branch matching the documented version, never `main`. Trace the operation actually reached by the test, including eager setup, fallbacks, caches, callbacks, and output construction. Source review informs the test but does not replace runnable evidence for claims execution can settle. ## Finish Run the focused module and claim tests while iterating, then: ```bash make check ``` Before declaring coverage complete, reconcile the claim inventory against the final page line by line. Report any category C claims and their source evidence, and any category D claims the running platform skipped; do not say "all claims tested" when some are only sourced, skipped elsewhere, or remain uncertain. This licenses nothing about `make check`, which does not run the API audit. Lint, types and tests must all pass before a commit, on every platform and without exception. A foreign-platform claim reaches a green suite by being guarded so it *skips*; an unguarded `import winreg` that breaks collection is a defect in the test to fix, never a failure to explain away. The only thing a foreign platform excuses is an inspection diagnostic in the audit's own output.