FA-79701 / Barcode symbology encoding / Open access

QR pad codewords start with 0x11 · case 01

Strict decoders flag the pad sequence and some generators produce different symbols for the same data.

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

ROOT CAUSE

The alternating pad bytes start with 0x11 instead of 0xEC.

THE FAILURE

The alternating pad bytes start with 0x11 instead of 0xEC.

Unsuccessful approach: Repeating 0xEC drops the alternation.

Case contract

Finish a QR data bit stream for a symbol with `capacity` data codewords: data longer than capacity*8 bits returns None; append a terminator of up to four 0 bits (truncated if capacity is reached), pad with 0 bits to a byte boundary, split into codewords, then append pad codewords alternating 0xEC, 0x11 starting with 0xEC until the capacity is filled.

Why this case matters

Retail, logistics, pharmacy and document workflows depend on encoders that produce exactly the module pattern, code-set switches, separators and quiet zones scanners expect; one misplaced module or separator makes a label unreadable or, worse, scan as different data.

1 / The failure

Exit 1

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

N = 1
observations = []
def solve(bits, capacity):
    cap = capacity * 8
    if len(bits) > cap:
        return None
    bits += '0' * min(4, cap - len(bits))
    bits += '0' * (-len(bits) % 8)
    words = [int(bits[i:i + 8], 2) for i in range(0, len(bits), 8)]
    pads = [0x11, 0xEC]
    k = 0
    while len(words) < capacity:
        words.append(pads[k % 2])
        k += 1
    return words
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[('011111010101101111101000', 5), [125, 91, 232, 0, 236]], [('11000111110010110000011101', 7), [199, 203, 7, 64, 236, 17, 236]], [('11111010', 4), [250, 0, 236, 17]], [('11110', 1), [240]], [('10111110000100110101101101110110', 4), [190, 19, 91, 118]], [('010010001010011000110010101000', 4), [72, 166, 50, 160]], [('01110101', 1), [117]], [('00001110010101111011010001101111', 6), [14, 87, 180, 111, 0, 236]]], [[('010111100111111111101011010111010101', 6), [94, 127, 235, 93, 80, 236]], [('1100010000000100010111100', 6), [196, 4, 94, 0, 236, 17]], [('0000011111000010', 5), [7, 194, 0, 236, 17]], [('10110001011011111', 2), None], [('00101010101111110010011110110010011100110', 5), None], [('000100', 1), [16]], [('0110001100001', 2), [99, 8]], [('1001000010010100100111111101111110011111', 7), [144, 148, 159, 223, 159, 0, 236]]], [[('0110000101010010', 7), [97, 82, 0, 236, 17, 236, 17]], [('00111100', 4), [60, 0, 236, 17]], [('10110101', 1), [181]], [('1011011', 1), [182]], [('011000110111001010011001100110101001', 5), [99, 114, 153, 154, 144]], [('0101101001011000000111111001001110110111010010101000', 7), [90, 88, 31, 147, 183, 74, 128]], [('11010010000000100000001', 4), [210, 2, 2, 0]], [('', 5), [0, 236, 17, 236, 17]]], [[('10110100010010101010', 6), [180, 74, 160, 236, 17, 236]], [('', 4), [0, 236, 17, 236]], [('00011', 1), [24]], [('0001101010100101110100100000001001110111000110110', 6), None], [('01000011', 2), [67, 0]], [('10110010', 2), [178, 0]], [('0011011001010010110110011110111001111101', 5), [54, 82, 217, 238, 125]], [('0011001110001010', 6), [51, 138, 0, 236, 17, 236]]], [[('10011001111', 6), [153, 224, 236, 17, 236, 17]], [('00110110', 5), [54, 0, 236, 17, 236]], [('1100010100110', 7), [197, 48, 0, 236, 17, 236, 17]], [('101000101010111110000010011010001', 4), None], [('010001110011110110111010011110011100001110101010100001101', 7), None], [('1001000010101110001111000110111010001110', 5), [144, 174, 60, 110, 142]], [('000101100', 1), None], [('11001010', 5), [202, 0, 236, 17, 236]]]]
labels = ["regression: pad codeword sequence", "repair trap", "combined fault", "control", "control", "boundary", "boundary", "control"]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (labels[i % len(labels)], i), solve(*args), expected)
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 fixture	Actual	Expected	Outcome
regression: pad codeword sequence 0	`[125, 91, 232, 0, 17]`	`[125, 91, 232, 0, 236]`	Failed
repair trap 1	`[199, 203, 7, 64, 17, 236, 17]`	`[199, 203, 7, 64, 236, 17, 236]`	Failed
combined fault 2	`[250, 0, 17, 236]`	`[250, 0, 236, 17]`	Failed
control 3	`[240]`	`[240]`	Passed
control 4	`[190, 19, 91, 118]`	`[190, 19, 91, 118]`	Passed
boundary 5	`[72, 166, 50, 160]`	`[72, 166, 50, 160]`	Passed
boundary 6	`[117]`	`[117]`	Passed
control 7	`[14, 87, 180, 111, 0, 17]`	`[14, 87, 180, 111, 0, 236]`	Failed

SHA-256 / dff8fd4bade37dc288b5df396ab60a4b2bb81231f52fd686ad5d05ede60b9751

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(bits, capacity):
    cap = capacity * 8
    if len(bits) > cap:
        return None
    bits += '0' * min(4, cap - len(bits))
    bits += '0' * (-len(bits) % 8)
    words = [int(bits[i:i + 8], 2) for i in range(0, len(bits), 8)]
    pads = [0xEC, 0xEC]
    k = 0
    while len(words) < capacity:
        words.append(pads[k % 2])
        k += 1
    return words
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[('011111010101101111101000', 5), [125, 91, 232, 0, 236]], [('11000111110010110000011101', 7), [199, 203, 7, 64, 236, 17, 236]], [('11111010', 4), [250, 0, 236, 17]], [('11110', 1), [240]], [('10111110000100110101101101110110', 4), [190, 19, 91, 118]], [('010010001010011000110010101000', 4), [72, 166, 50, 160]], [('01110101', 1), [117]], [('00001110010101111011010001101111', 6), [14, 87, 180, 111, 0, 236]]], [[('010111100111111111101011010111010101', 6), [94, 127, 235, 93, 80, 236]], [('1100010000000100010111100', 6), [196, 4, 94, 0, 236, 17]], [('0000011111000010', 5), [7, 194, 0, 236, 17]], [('10110001011011111', 2), None], [('00101010101111110010011110110010011100110', 5), None], [('000100', 1), [16]], [('0110001100001', 2), [99, 8]], [('1001000010010100100111111101111110011111', 7), [144, 148, 159, 223, 159, 0, 236]]], [[('0110000101010010', 7), [97, 82, 0, 236, 17, 236, 17]], [('00111100', 4), [60, 0, 236, 17]], [('10110101', 1), [181]], [('1011011', 1), [182]], [('011000110111001010011001100110101001', 5), [99, 114, 153, 154, 144]], [('0101101001011000000111111001001110110111010010101000', 7), [90, 88, 31, 147, 183, 74, 128]], [('11010010000000100000001', 4), [210, 2, 2, 0]], [('', 5), [0, 236, 17, 236, 17]]], [[('10110100010010101010', 6), [180, 74, 160, 236, 17, 236]], [('', 4), [0, 236, 17, 236]], [('00011', 1), [24]], [('0001101010100101110100100000001001110111000110110', 6), None], [('01000011', 2), [67, 0]], [('10110010', 2), [178, 0]], [('0011011001010010110110011110111001111101', 5), [54, 82, 217, 238, 125]], [('0011001110001010', 6), [51, 138, 0, 236, 17, 236]]], [[('10011001111', 6), [153, 224, 236, 17, 236, 17]], [('00110110', 5), [54, 0, 236, 17, 236]], [('1100010100110', 7), [197, 48, 0, 236, 17, 236, 17]], [('101000101010111110000010011010001', 4), None], [('010001110011110110111010011110011100001110101010100001101', 7), None], [('1001000010101110001111000110111010001110', 5), [144, 174, 60, 110, 142]], [('000101100', 1), None], [('11001010', 5), [202, 0, 236, 17, 236]]]]
labels = ["regression: pad codeword sequence", "repair trap", "combined fault", "control", "control", "boundary", "boundary", "control"]
for i, (args, expected) in enumerate(fixtures[N-1]):
    check("%s %d" % (labels[i % len(labels)], i), solve(*args), expected)
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 fixture	Actual	Expected	Outcome
regression: pad codeword sequence 0	`[125, 91, 232, 0, 236]`	`[125, 91, 232, 0, 236]`	Passed
repair trap 1	`[199, 203, 7, 64, 236, 236, 236]`	`[199, 203, 7, 64, 236, 17, 236]`	Failed
combined fault 2	`[250, 0, 236, 236]`	`[250, 0, 236, 17]`	Failed
control 3	`[240]`	`[240]`	Passed
control 4	`[190, 19, 91, 118]`	`[190, 19, 91, 118]`	Passed
boundary 5	`[72, 166, 50, 160]`	`[72, 166, 50, 160]`	Passed
boundary 6	`[117]`	`[117]`	Passed
control 7	`[14, 87, 180, 111, 0, 236]`	`[14, 87, 180, 111, 0, 236]`	Passed

SHA-256 / a6469e4ddd1e87521629c0f944c1f647518b7ef3ad3837b13eeab2578724dcae

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 8 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

A deterministic bounded teaching model with a stipulated contract; it makes no claim of conformance to any published specification. 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 2026-09-29T14:49:46.984801+00:00.

Case digest / 41d8da9ac35a6963bd0668ec30af01cadc0c0ca7f0aefffda9f0bf9c4dbb76ed