pytest-flakedoctor 0.1.2

The flaky-test doctor: diagnoses WHY a test is flaky and hands you a deterministic reproduction

pytest-flakedoctor

Every other tool retries your flaky test. flakedoctor tells you why it's flaky — and hands you a command that makes it fail every time.

Point it at a test that fails one run in twenty. It re-runs the test in fresh subprocesses under controlled perturbation, bisects which axis of nondeterminism triggers the failure, verifies the repro statistically, checks the counterfactual, and prints this:

━━━ flakedoctor ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
 test_billing.py::test_invoice_ids_stable
 py3.14.4 · darwin · 35 runs · 8.5s

 DIAGNOSIS  hash-order dependent (PYTHONHASHSEED)
   Failed 3/10 isolated baseline runs; fails 10/10 with PYTHONHASHSEED=2
   and passed the counterfactual with PYTHONHASHSEED=0. The test's
   outcome depends on Python's hash randomization: dict/set iteration
   order (or hash-based ordering) leaks into an assertion or into order-
   sensitive logic. PYTHONHASHSEED=2 pins the order that triggers the
   failure.

 EVIDENCE                                                  runs  failed
   baseline (isolated, no perturbation)                     10       3
   provoke: PYTHONHASHSEED=0                                 2       0
   provoke: PYTHONHASHSEED=1                                 2       0
   provoke: PYTHONHASHSEED=2                                 2       2
   provoke: PYTHONHASHSEED=3855983186                        2       2
   provoke: PYTHONHASHSEED=2386784001                        2       0
   VERIFY: PYTHONHASHSEED=2                                 10      10   ✓ deterministic
   counterfactual: PYTHONHASHSEED=0                          5       0   ✓ passes

 REPRO (fails 10/10; ≥74% repro rate at 95% confidence)
   PYTHONHASHSEED=2 pytest "test_billing.py::test_invoice_ids_stable"
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━

Three minutes ago this failed once a week. Now it fails on demand. Paste the command into the bug report and fix it.

Install

pip install pytest-flakedoctor                 # order, time, rng, network, fs, hashseed
pip install "pytest-flakedoctor[interleave]"   # + the thread-interleaving axis (Python ≥3.12)

Requires Python ≥3.10. Then point --doctor at a flaky test:

pytest path/to/test.py::test_name --doctor

Usage

pytest tests/test_billing.py::test_invoice_ids_stable --doctor   # diagnose this test
pytest --doctor                       # run the suite; diagnose the first failure
flakedoctor tests/test_x.py::test_y   # console-script sugar for the first form

Options (also settable via ini):

Flag	Default	Meaning
--doctor-runs=N	10	baseline sample size
--doctor-budget=SECS	300	wall-clock budget; the planner degrades gracefully
--doctor-json=PATH	—	machine-readable flakedoctor-report v1 (CI artifact)
--doctor-allow-side-effects	off	run even if the test makes network calls or spawns processes (see below)

Axes

Axis	What it catches	How it's controlled
order	a polluter test that leaks state the victim depends on being absent	run the victim after a shrinking prefix of the suite
interleave	thread races and deadlocks — a schedule that makes a threaded test fail	drive the test through a deterministic scheduler searching interleavings
time	month/day/year rollovers, DST, leap day, "assumes the code runs fast"	virtual clock frozen at adversarial instants, plus tick jitter
rng	unseeded random/secrets/uuid4, colliding fixture data	all randomness seeded from one seed
network	hidden live-network dependencies	outbound connections blocked
fs	ambient files, $HOME leftovers, cross-test residue	isolated filesystem tree
hashseed	dict/set iteration order leaking into behavior	PYTHONHASHSEED per subprocess

The four sandbox axes are powered by hermetic (installed automatically; distribution hermetic-sandbox, import name hermetic).

The interleave axis (opt-in)

For threaded tests, the doctor can search for the exact thread schedule that makes them fail — a race or a deadlock — using interleave-test's deterministic scheduler, then hand you a schedule that reproduces it every time:

 DIAGNOSIS  race condition (thread interleaving)
   The test passes 10/10 times on its own, but a specific thread
   interleaving makes it fail — a real race the scheduler usually hides.
   The found schedule reproduces it 3/3 times. Cause: AssertionError at
   test_counter.py:16 (lost update). Add synchronization ...

It's an opt-in extra — pip install pytest-flakedoctor[interleave] — and needs Python ≥3.12 (the core supports ≥3.10). It only activates when a test actually starts threads, verifies every finding by deterministic replay before claiming anything, and is honest about what it can't see: threads or locks created at import time, thread-pool internals, and C-level threads are outside the modelled primitives, so it reports "no failing interleaving under the modelled primitives," never "no race exists."

The order axis

Test-order dependence is the most common cause of flaky tests in Python, and its diagnosis is the most satisfying: not "something is nondeterministic" but a named culprit. Run the whole suite under --doctor; when a test passes alone but failed in the suite, the doctor runs it after the collected prefix, then binary-searches that prefix down to the polluter:

 DIAGNOSIS  test-order dependent
   The test passes 10/10 times on its own, but fails when run after the
   test test_registry.py::test_register_plugin — fails every run in this
   order. This is a test-order dependency: that test leaves behind state
   the victim depends on being absent.

 EVIDENCE                                                  runs  failed
   baseline (isolated, no perturbation)                     10       0
   after full suite prefix (4 tests)                         3       3
   VERIFY: after test_registry.py::test_register_plugin     10      10   ✓ deterministic

The repro command runs just [polluter, victim] in the order that fails.

How it works

Every diagnostic run is a fresh pytest subprocess — that buys clean module/fixture state per run, hang/crash immunity via OS kill, and axes that are impossible to vary in-process (hash randomization is fixed at interpreter start). The loop:

1. Baseline — N isolated, unperturbed runs. Deterministic failure and hangs are called out immediately; a mixed result means a live flake.
2. Control sweep — determinize every axis at once. If the flakiness disappears, the cause is one of them, and controlling each axis alone identifies which — the stabilizer. That axis gets swept first.
3. Provocation sweep — pin adversarial values on the suspect axis. Every failure is fingerprinted (phase, exception type, crash site) so a perturbation that breaks the test differently than the observed flake never earns causal credit — and failures raised by the sandbox itself (NetworkBlockedError and friends) are reported as perturbation-induced, never as your bug.
4. Verification gate — the winning value must fail n/n fresh runs. The report never claims 100%; it prints the count and the exact Clopper-Pearson lower bound.
5. Counterfactual gate — a benign value on the same axis must actually pass, or the verdict is downgraded to "elevates failure rate".

Reproducing

hermetic's CLI can express a seed but not a clock, network, or filesystem configuration, so a diagnosed perturbation travels as a compact blob:

pytest tests/test_billing.py::test_x --doctor-repro=fd1:eNodTs0KwjAYe5dc...

That applies the perturbation in-process, so you can attach a debugger and get a normal traceback. Blobs are versioned per axis and decode strictly: an unknown axis, a newer payload version, or a blob recorded for a different test fails loudly rather than quietly doing less than it claims. A repro that passes exits non-zero with DID NOT REPRODUCE (Hypothesis semantics) — staleness is never silent.

For the sandbox axes (time/rng/network/fs), the report also prints a marker:

@pytest.mark.flakedoctor_repro("fd1:eNoVTksKw...")
def test_invoice_period_label():
    ...

Paste it above the test and commit it. Now the flake reproduces deterministically on every ordinary pytest run — in CI and in code review — until someone fixes it, exactly like Hypothesis's @reproduce_failure. (It's inert during --doctor runs and in the doctor's own subprocesses; hashseed and order repros can't be re-applied by a marker, so those print the command to use instead.)

Verdicts are honest: flaky-time / flaky-rng / flaky-network / flaky-fs / flaky-hashseed (each either observed — it explains the failure you actually saw — or latent, a bug the doctor provoked), plus deterministic-failure, hang, flaky-unattributed ("real flake, cause not in covered axes — here's the evidence"), incomplete, and not-flaky.

The tool is built to refuse a diagnosis it can't support. An elevated failure rate has to clear a significance test against the baseline rate, so an ordinary coin-flip flake never gets blamed on whichever hash seed happened to streak; "passing" evidence must come from runs that actually executed the test, not merely from runs that didn't fail; and any provocation failure left unattributed is reported rather than dropped.

Status

Alpha, published on PyPI. Working today: the subprocess engine, the full control → provoke → verify → counterfactual loop, all seven axes (order, interleave, time, rng, network, fs, hashseed), failure fingerprinting, repro blobs, the repro marker, terminal + JSON reports, and suite mode.

Roadmap (see DESIGN.md for the full architecture):

• later — in-process fast path for slow-import suites; xdist-parallel order simulation; free-threading safety auditor (flakedoctor audit <package> on 3.13t/3.14t); GitHub Action posting diagnoses on PRs.

Known gaps, stated plainly: async tests skip the clock axis (a virtual clock hangs awaited sleeps), and async-wait is a large share of real-world flakes; and a time-based repro may not transfer across machines unless TZ is set, which the report warns about.

Side effects are gated: if the first run makes an outbound network connection (to a non-loopback host) or spawns a subprocess, the doctor stops after that single run rather than repeating the effect across dozens — pass --doctor-allow-side-effects to proceed when the test is safe to repeat. (Detection is conservative: it covers socket.connect and subprocess, not raw file writes or C-level I/O.)

Development

PYTHONPATH=src python -m pytest tests/          # full suite (~1 min; spawns real children)
PYTHONPATH=src python -m pytest tests/ -k "not e2e and not runner"   # fast logic tests

MIT license.