FAILURE MAP
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FA-10131 / Deployment models / Open access

Replica rollout status: Invalid specifications appear ready · case 01

Invalid specifications appear ready.

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

ROOT CAUSE

The implementation substitutes if False: for if not valid:, so invalid specifications appear ready.

VERIFIED REPAIR

Invalid specifications block rollout.

Unsuccessful approach: The attempted repair substitutes if valid:. Fixture 1 still yields 'ready' instead of 'blocked'.

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 False: 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 1readyblockedFailed
fixture 2degradeddegradedPassed
fixture 3progressingprogressingPassed
fixture 4progressingprogressingPassed
fixture 5progressingprogressingPassed
fixture 6readyreadyPassed
fixture 7readyreadyPassed
fixture 8readyreadyPassed

SHA-256 / 100a5404dfd6ba720a21f2bedf26b5cf3abc7a47ceab14923d6275e45f3211d9

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 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 1readyblockedFailed
fixture 2blockeddegradedFailed
fixture 3blockedprogressingFailed
fixture 4blockedprogressingFailed
fixture 5blockedprogressingFailed
fixture 6blockedreadyFailed
fixture 7blockedreadyFailed
fixture 8blockedreadyFailed

SHA-256 / 37b9938cf9394c6ac8569aa6b6b77f11c824639f1d7502c755ef0f8da66d7509

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 2026-09-29T14:38:36.038691+00:00.

Case digest / 98016b93c111b5b529fe9390922fad7a1ef9238dd59dbd35c11e293e34e4ea51