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FA-18191 / Time representation / Open access

Timestamp migration confuses epoch zero with absent value · case 01

The decoded time state disagrees with the explicit regression oracle for present.

Verified by executionVariant 1 · 15 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Timestamp migration confuses epoch zero with absent value.

THE FAILURE

Timestamp migration confuses epoch zero with absent value.

Unsuccessful approach: The partial correction still substitutes r['present'] and r['value'] != 0 at the same fault site.

Case contract

Controlled timestamp migration from version 1 milliseconds or version 2 split seconds/nanos into exact nanoseconds. A separate presence bit distinguishes missing from epoch zero. Version 2 fraction must be 0..999999999; timezone offsets are forbidden because payload is already UTC. Preserve uncertainty in matching units and emit canonical seconds/remainder with precision and provenance. Negative uncertainty is invalid; zero means exact.

Why this case matters

Clock transfer and timestamp consumers require preserved coordinate, phase, validity and elapsed-time semantics.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(r):
    present = bool(r['value'])
    version_ok = r['version'] in (1,2)
    fraction_ok = r['version'] != 2 or 0 <= r['fraction'] < 1000000000
    offset_ok = r['offset'] is None
    valid = version_ok and fraction_ok and offset_ok and r['uncertainty'] >= 0
    nanoseconds = r['value'] * 1000000 if r['version'] == 1 else r['value'] * 1000000000 + r['fraction']
    uncertainty = r['uncertainty'] * (1000000 if r['version'] == 1 else 1)
    seconds = nanoseconds // 1000000000
    remainder = nanoseconds - seconds * 1000000000
    precision = 1000000 if r['version'] == 1 else 1
    return ['missing' if not present else ('invalid' if not valid else 'ok'), [seconds,remainder,uncertainty,precision] if present and valid else None]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve({'present': True, 'version': 1, 'value': 0, 'fraction': 0, 'offset': None, 'uncertainty': 2}), ['ok', [0, 0, 2000000, 1000000]])
check('fixture 2', solve({'present': False, 'version': 1, 'value': 20, 'fraction': 0, 'offset': None, 'uncertainty': 1}), ['missing', None])
check('fixture 3', solve({'present': True, 'version': 1, 'value': 1234, 'fraction': 0, 'offset': None, 'uncertainty': 3}), ['ok', [1, 234000000, 3000000, 1000000]])
check('fixture 4', solve({'present': True, 'version': 2, 'value': -1, 'fraction': 500000000, 'offset': None, 'uncertainty': 4}), ['ok', [-1, 500000000, 4, 1]])
check('fixture 5', solve({'present': True, 'version': 2, 'value': 3, 'fraction': 0, 'offset': None, 'uncertainty': 2}), ['ok', [3, 0, 2, 1]])
check('fixture 6', solve({'present': True, 'version': 2, 'value': 1, 'fraction': 1000000000, 'offset': None, 'uncertainty': 2}), ['invalid', None])
check('fixture 7', solve({'present': True, 'version': 2, 'value': 1, 'fraction': -1, 'offset': None, 'uncertainty': 2}), ['invalid', None])
check('fixture 8', solve({'present': True, 'version': 3, 'value': 1, 'fraction': 0, 'offset': None, 'uncertainty': 1}), ['invalid', None])
check('fixture 9', solve({'present': True, 'version': 2, 'value': 1, 'fraction': 0, 'offset': 0, 'uncertainty': 1}), ['invalid', None])
check('fixture 10', solve({'present': True, 'version': 1, 'value': 1, 'fraction': 0, 'offset': 60, 'uncertainty': 1}), ['invalid', None])
check('fixture 11', solve({'present': True, 'version': 1, 'value': -1, 'fraction': 0, 'offset': None, 'uncertainty': 2}), ['ok', [-1, 999000000, 2000000, 1000000]])
check('fixture 12', solve({'present': True, 'version': 1, 'value': 1234, 'fraction': 500, 'offset': None, 'uncertainty': 2}), ['ok', [1, 234000000, 2000000, 1000000]])
check('fixture 13', solve({'present': True, 'version': 2, 'value': 1, 'fraction': 0, 'offset': None, 'uncertainty': -1}), ['invalid', None])
check('fixture 14', solve({'present': True, 'version': 2, 'value': 1, 'fraction': 0, 'offset': None, 'uncertainty': 0}), ['ok', [1, 0, 0, 1]])
variant = [({'present': True, 'version': 1, 'value': 1, 'fraction': 0, 'offset': None, 'uncertainty': 2}, ['ok', [0, 1000000, 2000000, 1000000]]), ({'present': True, 'version': 1, 'value': 2, 'fraction': 0, 'offset': None, 'uncertainty': 2}, ['ok', [0, 2000000, 2000000, 1000000]]), ({'present': True, 'version': 1, 'value': 3, 'fraction': 0, 'offset': None, 'uncertainty': 2}, ['ok', [0, 3000000, 2000000, 1000000]]), ({'present': True, 'version': 1, 'value': 4, 'fraction': 0, 'offset': None, 'uncertainty': 2}, ['ok', [0, 4000000, 2000000, 1000000]]), ({'present': True, 'version': 1, 'value': 5, 'fraction': 0, 'offset': None, 'uncertainty': 2}, ['ok', [0, 5000000, 2000000, 1000000]])]
check("variant capture", solve(variant[N-1][0]), variant[N-1][1])
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 1['missing', None]['ok', [0, 0, 2000000, 1000000]]Failed
fixture 2['ok', [0, 20000000, 1000000, 1000000]]['missing', None]Failed
fixture 3['ok', [1, 234000000, 3000000, 1000000]]['ok', [1, 234000000, 3000000, 1000000]]Passed
fixture 4['ok', [-1, 500000000, 4, 1]]['ok', [-1, 500000000, 4, 1]]Passed
fixture 5['ok', [3, 0, 2, 1]]['ok', [3, 0, 2, 1]]Passed
fixture 6['invalid', None]['invalid', None]Passed
fixture 7['invalid', None]['invalid', None]Passed
fixture 8['invalid', None]['invalid', None]Passed
fixture 9['invalid', None]['invalid', None]Passed
fixture 10['invalid', None]['invalid', None]Passed
fixture 11['ok', [-1, 999000000, 2000000, 1000000]]['ok', [-1, 999000000, 2000000, 1000000]]Passed
fixture 12['ok', [1, 234000000, 2000000, 1000000]]['ok', [1, 234000000, 2000000, 1000000]]Passed
fixture 13['invalid', None]['invalid', None]Passed
fixture 14['ok', [1, 0, 0, 1]]['ok', [1, 0, 0, 1]]Passed
variant capture['ok', [0, 1000000, 2000000, 1000000]]['ok', [0, 1000000, 2000000, 1000000]]Passed

SHA-256 / e4c36d95638c92d305e1ae374e30b7d0ea79581b81eac102c8c5a49badade890

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(r):
    present = r['present'] and r['value'] != 0
    version_ok = r['version'] in (1,2)
    fraction_ok = r['version'] != 2 or 0 <= r['fraction'] < 1000000000
    offset_ok = r['offset'] is None
    valid = version_ok and fraction_ok and offset_ok and r['uncertainty'] >= 0
    nanoseconds = r['value'] * 1000000 if r['version'] == 1 else r['value'] * 1000000000 + r['fraction']
    uncertainty = r['uncertainty'] * (1000000 if r['version'] == 1 else 1)
    seconds = nanoseconds // 1000000000
    remainder = nanoseconds - seconds * 1000000000
    precision = 1000000 if r['version'] == 1 else 1
    return ['missing' if not present else ('invalid' if not valid else 'ok'), [seconds,remainder,uncertainty,precision] if present and valid else None]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve({'present': True, 'version': 1, 'value': 0, 'fraction': 0, 'offset': None, 'uncertainty': 2}), ['ok', [0, 0, 2000000, 1000000]])
check('fixture 2', solve({'present': False, 'version': 1, 'value': 20, 'fraction': 0, 'offset': None, 'uncertainty': 1}), ['missing', None])
check('fixture 3', solve({'present': True, 'version': 1, 'value': 1234, 'fraction': 0, 'offset': None, 'uncertainty': 3}), ['ok', [1, 234000000, 3000000, 1000000]])
check('fixture 4', solve({'present': True, 'version': 2, 'value': -1, 'fraction': 500000000, 'offset': None, 'uncertainty': 4}), ['ok', [-1, 500000000, 4, 1]])
check('fixture 5', solve({'present': True, 'version': 2, 'value': 3, 'fraction': 0, 'offset': None, 'uncertainty': 2}), ['ok', [3, 0, 2, 1]])
check('fixture 6', solve({'present': True, 'version': 2, 'value': 1, 'fraction': 1000000000, 'offset': None, 'uncertainty': 2}), ['invalid', None])
check('fixture 7', solve({'present': True, 'version': 2, 'value': 1, 'fraction': -1, 'offset': None, 'uncertainty': 2}), ['invalid', None])
check('fixture 8', solve({'present': True, 'version': 3, 'value': 1, 'fraction': 0, 'offset': None, 'uncertainty': 1}), ['invalid', None])
check('fixture 9', solve({'present': True, 'version': 2, 'value': 1, 'fraction': 0, 'offset': 0, 'uncertainty': 1}), ['invalid', None])
check('fixture 10', solve({'present': True, 'version': 1, 'value': 1, 'fraction': 0, 'offset': 60, 'uncertainty': 1}), ['invalid', None])
check('fixture 11', solve({'present': True, 'version': 1, 'value': -1, 'fraction': 0, 'offset': None, 'uncertainty': 2}), ['ok', [-1, 999000000, 2000000, 1000000]])
check('fixture 12', solve({'present': True, 'version': 1, 'value': 1234, 'fraction': 500, 'offset': None, 'uncertainty': 2}), ['ok', [1, 234000000, 2000000, 1000000]])
check('fixture 13', solve({'present': True, 'version': 2, 'value': 1, 'fraction': 0, 'offset': None, 'uncertainty': -1}), ['invalid', None])
check('fixture 14', solve({'present': True, 'version': 2, 'value': 1, 'fraction': 0, 'offset': None, 'uncertainty': 0}), ['ok', [1, 0, 0, 1]])
variant = [({'present': True, 'version': 1, 'value': 1, 'fraction': 0, 'offset': None, 'uncertainty': 2}, ['ok', [0, 1000000, 2000000, 1000000]]), ({'present': True, 'version': 1, 'value': 2, 'fraction': 0, 'offset': None, 'uncertainty': 2}, ['ok', [0, 2000000, 2000000, 1000000]]), ({'present': True, 'version': 1, 'value': 3, 'fraction': 0, 'offset': None, 'uncertainty': 2}, ['ok', [0, 3000000, 2000000, 1000000]]), ({'present': True, 'version': 1, 'value': 4, 'fraction': 0, 'offset': None, 'uncertainty': 2}, ['ok', [0, 4000000, 2000000, 1000000]]), ({'present': True, 'version': 1, 'value': 5, 'fraction': 0, 'offset': None, 'uncertainty': 2}, ['ok', [0, 5000000, 2000000, 1000000]])]
check("variant capture", solve(variant[N-1][0]), variant[N-1][1])
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 1['missing', None]['ok', [0, 0, 2000000, 1000000]]Failed
fixture 2['missing', None]['missing', None]Passed
fixture 3['ok', [1, 234000000, 3000000, 1000000]]['ok', [1, 234000000, 3000000, 1000000]]Passed
fixture 4['ok', [-1, 500000000, 4, 1]]['ok', [-1, 500000000, 4, 1]]Passed
fixture 5['ok', [3, 0, 2, 1]]['ok', [3, 0, 2, 1]]Passed
fixture 6['invalid', None]['invalid', None]Passed
fixture 7['invalid', None]['invalid', None]Passed
fixture 8['invalid', None]['invalid', None]Passed
fixture 9['invalid', None]['invalid', None]Passed
fixture 10['invalid', None]['invalid', None]Passed
fixture 11['ok', [-1, 999000000, 2000000, 1000000]]['ok', [-1, 999000000, 2000000, 1000000]]Passed
fixture 12['ok', [1, 234000000, 2000000, 1000000]]['ok', [1, 234000000, 2000000, 1000000]]Passed
fixture 13['invalid', None]['invalid', None]Passed
fixture 14['ok', [1, 0, 0, 1]]['ok', [1, 0, 0, 1]]Passed
variant capture['ok', [0, 1000000, 2000000, 1000000]]['ok', [0, 1000000, 2000000, 1000000]]Passed

SHA-256 / 7353edd4f9002beca2b8bd663b2a4d26813114a0b7b1c4e0a42918bcf93b94d6

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 15 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

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Verification & scope

Deterministic integer reference model with stipulated units and policies; not a complete clock, wire standard or platform implementation. 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 2026-09-29T14:39:55.514639+00:00.

Case digest / ac6e30f8074d5d6b96b734663af7b6fc02d67bb865dff1638f612ddfded0a786