FAILURE MAP
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Replica rollout status: Failed replicas do not degrade rollout · case 01

Failed replicas do not degrade rollout.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The implementation substitutes if False: for if failed:, so failed replicas do not degrade rollout.

VERIFIED REPAIR

Surface required replica failures.

Unsuccessful approach: The attempted repair substitutes if not failed:. Fixture 2 still yields 'ready' instead of 'degraded'.

Case contract

Report blocked on invalid specification, degraded on failed required replicas, progressing when observed generation lags, progressing when updated or available counts are below desired, otherwise ready. Zero desired is ready when observed.

Why this case matters

An offline model of replica rollout status, suitable for testing build and release tooling without external services.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(valid, failed, observed, generation, updated, available, desired):
    if not valid: return 'blocked'
    if False: return 'degraded'
    if observed < generation: return 'progressing'
    if updated < desired: return 'progressing'
    if available < desired: return 'progressing'
    return 'ready'
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve(False, False, 1, 1, 1, 1, 1), 'blocked')
check('fixture 2', solve(True, True, 1, 1, 1, 1, 1), 'degraded')
check('fixture 3', solve(True, False, 0, 1, 1, 1, 1), 'progressing')
check('fixture 4', solve(True, False, 1, 1, 0, 1, 1), 'progressing')
check('fixture 5', solve(True, False, 1, 1, 1, 0, 1), 'progressing')
check('fixture 6', solve(True, False, 1, 1, 1, 1, 1), 'ready')
check('fixture 7', solve(True, False, 1, 1, 0, 0, 0), 'ready')
check('fixture 8', solve(True, False, 2, 1, 2, 2, 1), 'ready')
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
fixture 1blockedblockedPassed
fixture 2readydegradedFailed
fixture 3progressingprogressingPassed
fixture 4progressingprogressingPassed
fixture 5progressingprogressingPassed
fixture 6readyreadyPassed
fixture 7readyreadyPassed
fixture 8readyreadyPassed

SHA-256 / 2fd3c62997285cad1e05e21df210c1a53e2be6c1afd475fdb516019ed89c6f04

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(valid, failed, observed, generation, updated, available, desired):
    if not valid: return 'blocked'
    if not failed: return 'degraded'
    if observed < generation: return 'progressing'
    if updated < desired: return 'progressing'
    if available < desired: return 'progressing'
    return 'ready'
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve(False, False, 1, 1, 1, 1, 1), 'blocked')
check('fixture 2', solve(True, True, 1, 1, 1, 1, 1), 'degraded')
check('fixture 3', solve(True, False, 0, 1, 1, 1, 1), 'progressing')
check('fixture 4', solve(True, False, 1, 1, 0, 1, 1), 'progressing')
check('fixture 5', solve(True, False, 1, 1, 1, 0, 1), 'progressing')
check('fixture 6', solve(True, False, 1, 1, 1, 1, 1), 'ready')
check('fixture 7', solve(True, False, 1, 1, 0, 0, 0), 'ready')
check('fixture 8', solve(True, False, 2, 1, 2, 2, 1), 'ready')
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
fixture 1blockedblockedPassed
fixture 2readydegradedFailed
fixture 3degradedprogressingFailed
fixture 4degradedprogressingFailed
fixture 5degradedprogressingFailed
fixture 6degradedreadyFailed
fixture 7degradedreadyFailed
fixture 8degradedreadyFailed

SHA-256 / 4ace869bce5d625370dc534119af7f0c3e28485c110f0c3dabe68c5343cbecec

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(valid, failed, observed, generation, updated, available, desired):
    if not valid: return 'blocked'
    if failed: return 'degraded'
    if observed < generation: return 'progressing'
    if updated < desired: return 'progressing'
    if available < desired: return 'progressing'
    return 'ready'
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve(False, False, 1, 1, 1, 1, 1), 'blocked')
check('fixture 2', solve(True, True, 1, 1, 1, 1, 1), 'degraded')
check('fixture 3', solve(True, False, 0, 1, 1, 1, 1), 'progressing')
check('fixture 4', solve(True, False, 1, 1, 0, 1, 1), 'progressing')
check('fixture 5', solve(True, False, 1, 1, 1, 0, 1), 'progressing')
check('fixture 6', solve(True, False, 1, 1, 1, 1, 1), 'ready')
check('fixture 7', solve(True, False, 1, 1, 0, 0, 0), 'ready')
check('fixture 8', solve(True, False, 2, 1, 2, 2, 1), 'ready')
print(json.dumps({"observations": observations, "passed": all(x["passed"] for x in observations)}, ensure_ascii=False))
raise SystemExit(0 if all(x["passed"] for x in observations) else 1)
Boundary fixtureActualExpectedOutcome
fixture 1blockedblockedPassed
fixture 2degradeddegradedPassed
fixture 3progressingprogressingPassed
fixture 4progressingprogressingPassed
fixture 5progressingprogressingPassed
fixture 6readyreadyPassed
fixture 7readyreadyPassed
fixture 8readyreadyPassed

SHA-256 / ec4f78ec9cf74394799a50ebfe7fd63525c297579689fdb23b5cdc789a04ad0a

Verification & scope

This reproducer isolates one failure mechanism. Results cover the supplied fixtures. Variants within a family share a test contract and should remain grouped when constructing evaluation splits. Related mechanisms with a shared evaluation_group must also remain together; these controlled models are not independent production incidents.

Observations recorded using Python 3.12.14 at 2026-09-29T14:38:36.110197+00:00.

Case digest / d08aaf08529aa8d41b7a87ca845ebf4e4f9fb78878976a77fbb7021f4e65f39f