FAILURE MAP
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FA-17416 / Time representation / Open access

Timestamp sign marker misses the minimum code or negative subsecond instants · case 01

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

Verified by executionVariant 1 · 11 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Timestamp sign marker misses the minimum code or negative subsecond instants.

VERIFIED REPAIR

Preserve the declared coordinate and state contract at negative: negative = total < 0.

Unsuccessful approach: The partial correction still substitutes total < -1000 at the same fault site.

Case contract

Decode four unsigned bytes: signed 16-bit big-endian seconds and unsigned 16-bit fractional microticks, 1000 per second. Fraction >=1000 is invalid. Return validity and, only for valid input, exact total microticks, signed decimal timestamp with three fractional digits, and whole-second coordinate after a one-microtick increment. Negative display uses magnitude decomposition.

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):
    word = r[0] * 256 + r[1]
    seconds = word - 65536 if word >= 32768 else word
    fraction = r[2] * 256 + r[3]
    valid = fraction < 1000
    total = seconds * 1000 + fraction
    negative = word > 32768
    magnitude = abs(total)
    whole = magnitude // 1000
    part = magnitude % 1000
    next_second = seconds + (fraction + 1) // 1000
    return [valid, total if valid else None, ('-' if negative else '') + str(whole) + '.' + str(part).zfill(3) if valid else None,next_second if valid else None]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve([0, 1, 0, 0]), [True, 1000, '1.000', 1])
check('fixture 2', solve([255, 255, 0, 1]), [True, -999, '-0.999', -1])
check('fixture 3', solve([128, 0, 3, 231]), [True, -32767001, '-32767.001', -32767])
check('fixture 4', solve([0, 0, 0, 0]), [True, 0, '0.000', 0])
check('fixture 5', solve([0, 0, 3, 232]), [False, None, None, None])
check('fixture 6', solve([255, 255, 3, 232]), [False, None, None, None])
check('fixture 7', solve([0, 2, 1, 3]), [True, 2259, '2.259', 2])
check('fixture 8', solve([127, 255, 3, 231]), [True, 32767999, '32767.999', 32768])
check('fixture 9', solve([255, 254, 0, 0]), [True, -2000, '-2.000', -2])
check('fixture 10', solve([0, 0, 3, 231]), [True, 999, '0.999', 1])
variant = [([0, 2, 0, 0], [True, 2000, '2.000', 2]), ([0, 3, 0, 0], [True, 3000, '3.000', 3]), ([0, 4, 0, 0], [True, 4000, '4.000', 4]), ([0, 5, 0, 0], [True, 5000, '5.000', 5]), ([0, 6, 0, 0], [True, 6000, '6.000', 6])]
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[True, 1000, '1.000', 1][True, 1000, '1.000', 1]Passed
fixture 2[True, -999, '-0.999', -1][True, -999, '-0.999', -1]Passed
fixture 3[True, -32767001, '32767.001', -32767][True, -32767001, '-32767.001', -32767]Failed
fixture 4[True, 0, '0.000', 0][True, 0, '0.000', 0]Passed
fixture 5[False, None, None, None][False, None, None, None]Passed
fixture 6[False, None, None, None][False, None, None, None]Passed
fixture 7[True, 2259, '2.259', 2][True, 2259, '2.259', 2]Passed
fixture 8[True, 32767999, '32767.999', 32768][True, 32767999, '32767.999', 32768]Passed
fixture 9[True, -2000, '-2.000', -2][True, -2000, '-2.000', -2]Passed
fixture 10[True, 999, '0.999', 1][True, 999, '0.999', 1]Passed
variant capture[True, 2000, '2.000', 2][True, 2000, '2.000', 2]Passed

SHA-256 / 4ad2e7247a0693bae6f5b9182ff7345f7fae54c66528ea179b995b9bc47b9172

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):
    word = r[0] * 256 + r[1]
    seconds = word - 65536 if word >= 32768 else word
    fraction = r[2] * 256 + r[3]
    valid = fraction < 1000
    total = seconds * 1000 + fraction
    negative = total < -1000
    magnitude = abs(total)
    whole = magnitude // 1000
    part = magnitude % 1000
    next_second = seconds + (fraction + 1) // 1000
    return [valid, total if valid else None, ('-' if negative else '') + str(whole) + '.' + str(part).zfill(3) if valid else None,next_second if valid else None]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve([0, 1, 0, 0]), [True, 1000, '1.000', 1])
check('fixture 2', solve([255, 255, 0, 1]), [True, -999, '-0.999', -1])
check('fixture 3', solve([128, 0, 3, 231]), [True, -32767001, '-32767.001', -32767])
check('fixture 4', solve([0, 0, 0, 0]), [True, 0, '0.000', 0])
check('fixture 5', solve([0, 0, 3, 232]), [False, None, None, None])
check('fixture 6', solve([255, 255, 3, 232]), [False, None, None, None])
check('fixture 7', solve([0, 2, 1, 3]), [True, 2259, '2.259', 2])
check('fixture 8', solve([127, 255, 3, 231]), [True, 32767999, '32767.999', 32768])
check('fixture 9', solve([255, 254, 0, 0]), [True, -2000, '-2.000', -2])
check('fixture 10', solve([0, 0, 3, 231]), [True, 999, '0.999', 1])
variant = [([0, 2, 0, 0], [True, 2000, '2.000', 2]), ([0, 3, 0, 0], [True, 3000, '3.000', 3]), ([0, 4, 0, 0], [True, 4000, '4.000', 4]), ([0, 5, 0, 0], [True, 5000, '5.000', 5]), ([0, 6, 0, 0], [True, 6000, '6.000', 6])]
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[True, 1000, '1.000', 1][True, 1000, '1.000', 1]Passed
fixture 2[True, -999, '0.999', -1][True, -999, '-0.999', -1]Failed
fixture 3[True, -32767001, '-32767.001', -32767][True, -32767001, '-32767.001', -32767]Passed
fixture 4[True, 0, '0.000', 0][True, 0, '0.000', 0]Passed
fixture 5[False, None, None, None][False, None, None, None]Passed
fixture 6[False, None, None, None][False, None, None, None]Passed
fixture 7[True, 2259, '2.259', 2][True, 2259, '2.259', 2]Passed
fixture 8[True, 32767999, '32767.999', 32768][True, 32767999, '32767.999', 32768]Passed
fixture 9[True, -2000, '-2.000', -2][True, -2000, '-2.000', -2]Passed
fixture 10[True, 999, '0.999', 1][True, 999, '0.999', 1]Passed
variant capture[True, 2000, '2.000', 2][True, 2000, '2.000', 2]Passed

SHA-256 / 96104d7269619535a61192169e6a052841784fc670030f9e2ee6ee124319986e

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json
from fractions import Fraction
N = 1
observations = []
def solve(r):
    word = r[0] * 256 + r[1]
    seconds = word - 65536 if word >= 32768 else word
    fraction = r[2] * 256 + r[3]
    valid = fraction < 1000
    total = seconds * 1000 + fraction
    negative = total < 0
    magnitude = abs(total)
    whole = magnitude // 1000
    part = magnitude % 1000
    next_second = seconds + (fraction + 1) // 1000
    return [valid, total if valid else None, ('-' if negative else '') + str(whole) + '.' + str(part).zfill(3) if valid else None,next_second if valid else None]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
check('fixture 1', solve([0, 1, 0, 0]), [True, 1000, '1.000', 1])
check('fixture 2', solve([255, 255, 0, 1]), [True, -999, '-0.999', -1])
check('fixture 3', solve([128, 0, 3, 231]), [True, -32767001, '-32767.001', -32767])
check('fixture 4', solve([0, 0, 0, 0]), [True, 0, '0.000', 0])
check('fixture 5', solve([0, 0, 3, 232]), [False, None, None, None])
check('fixture 6', solve([255, 255, 3, 232]), [False, None, None, None])
check('fixture 7', solve([0, 2, 1, 3]), [True, 2259, '2.259', 2])
check('fixture 8', solve([127, 255, 3, 231]), [True, 32767999, '32767.999', 32768])
check('fixture 9', solve([255, 254, 0, 0]), [True, -2000, '-2.000', -2])
check('fixture 10', solve([0, 0, 3, 231]), [True, 999, '0.999', 1])
variant = [([0, 2, 0, 0], [True, 2000, '2.000', 2]), ([0, 3, 0, 0], [True, 3000, '3.000', 3]), ([0, 4, 0, 0], [True, 4000, '4.000', 4]), ([0, 5, 0, 0], [True, 5000, '5.000', 5]), ([0, 6, 0, 0], [True, 6000, '6.000', 6])]
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[True, 1000, '1.000', 1][True, 1000, '1.000', 1]Passed
fixture 2[True, -999, '-0.999', -1][True, -999, '-0.999', -1]Passed
fixture 3[True, -32767001, '-32767.001', -32767][True, -32767001, '-32767.001', -32767]Passed
fixture 4[True, 0, '0.000', 0][True, 0, '0.000', 0]Passed
fixture 5[False, None, None, None][False, None, None, None]Passed
fixture 6[False, None, None, None][False, None, None, None]Passed
fixture 7[True, 2259, '2.259', 2][True, 2259, '2.259', 2]Passed
fixture 8[True, 32767999, '32767.999', 32768][True, 32767999, '32767.999', 32768]Passed
fixture 9[True, -2000, '-2.000', -2][True, -2000, '-2.000', -2]Passed
fixture 10[True, 999, '0.999', 1][True, 999, '0.999', 1]Passed
variant capture[True, 2000, '2.000', 2][True, 2000, '2.000', 2]Passed

SHA-256 / 6a0b68021316fa2c78faae39a976ebeefa970eae96bebc4cd1a690315d9557bb

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 2026-09-29T14:39:46.173156+00:00.

Case digest / 4f75853ac00297a7cb82ec5ec8f45d1031f8d9b2a1a538cbbdd3842b47f2ddd4