FA-79711 / Barcode symbology encoding / Open access

Pad alternation keyed on codeword position · case 01

Symbols with an odd number of data codewords start padding with 0x11.

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

ROOT CAUSE

The pad byte is selected by the absolute codeword index rather than by the pad count.

VERIFIED REPAIR

Alternate from the first pad codeword regardless of how many data codewords precede it.

Unsuccessful approach: Shifting the index by one breaks the even-length case instead.

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 = [0xEC, 0x11]
    k = 0
    while len(words) < capacity:
        words.append(pads[len(words) % 2])
        k += 1
    return words
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[('00001110010101111011010001101111', 6), [14, 87, 180, 111, 0, 236]], [('011111010101101111101000', 5), [125, 91, 232, 0, 236]], [('11110', 1), [240]], [('10111110000100110101101101110110', 4), [190, 19, 91, 118]], [('010010001010011000110010101000', 4), [72, 166, 50, 160]], [('01110101', 1), [117]], [('0101100111101', 2), [89, 232]], [('0111110011111101', 5), [124, 253, 0, 236, 17]]], [[('010111100111111111101011010111010101', 6), [94, 127, 235, 93, 80, 236]], [('1001000010010100100111111101111110011111', 7), [144, 148, 159, 223, 159, 0, 236]], [('10110001011011111', 2), None], [('00101010101111110010011110110010011100110', 5), None], [('000100', 1), [16]], [('0110001100001', 2), [99, 8]], [('1110111100000001110000101', 3), None], [('', 4), [0, 236, 17, 236]]], [[('', 3), [0, 236, 17]], [('00110011', 7), [51, 0, 236, 17, 236, 17, 236]], [('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]], [('0110000101010010', 7), [97, 82, 0, 236, 17, 236, 17]]], [[('10110100010010101010', 6), [180, 74, 160, 236, 17, 236]], [('011101111111100011100011', 7), [119, 248, 227, 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]]], [[('1111000000101011111011101010001011', 7), [240, 43, 238, 162, 192, 236, 17]], [('00110110', 5), [54, 0, 236, 17, 236]], [('101000101010111110000010011010001', 4), None], [('010001110011110110111010011110011100001110101010100001101', 7), None], [('1001000010101110001111000110111010001110', 5), [144, 174, 60, 110, 142]], [('000101100', 1), None], [('10111101', 1), [189]], [('', 2), [0, 236]]]]
labels = ["regression: pad alternation index", "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 alternation index 0	`[14, 87, 180, 111, 0, 17]`	`[14, 87, 180, 111, 0, 236]`	Failed
repair trap 1	`[125, 91, 232, 0, 236]`	`[125, 91, 232, 0, 236]`	Passed
combined fault 2	`[240]`	`[240]`	Passed
control 3	`[190, 19, 91, 118]`	`[190, 19, 91, 118]`	Passed
control 4	`[72, 166, 50, 160]`	`[72, 166, 50, 160]`	Passed
boundary 5	`[117]`	`[117]`	Passed
boundary 6	`[89, 232]`	`[89, 232]`	Passed
control 7	`[124, 253, 0, 17, 236]`	`[124, 253, 0, 236, 17]`	Failed

SHA-256 / 9a0e53c4ec8498cca7c171c6981fc628bb479b949b924df5a8f27841914499e2

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, 0x11]
    k = 0
    while len(words) < capacity:
        words.append(pads[(len(words) + 1) % 2])
        k += 1
    return words
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[('00001110010101111011010001101111', 6), [14, 87, 180, 111, 0, 236]], [('011111010101101111101000', 5), [125, 91, 232, 0, 236]], [('11110', 1), [240]], [('10111110000100110101101101110110', 4), [190, 19, 91, 118]], [('010010001010011000110010101000', 4), [72, 166, 50, 160]], [('01110101', 1), [117]], [('0101100111101', 2), [89, 232]], [('0111110011111101', 5), [124, 253, 0, 236, 17]]], [[('010111100111111111101011010111010101', 6), [94, 127, 235, 93, 80, 236]], [('1001000010010100100111111101111110011111', 7), [144, 148, 159, 223, 159, 0, 236]], [('10110001011011111', 2), None], [('00101010101111110010011110110010011100110', 5), None], [('000100', 1), [16]], [('0110001100001', 2), [99, 8]], [('1110111100000001110000101', 3), None], [('', 4), [0, 236, 17, 236]]], [[('', 3), [0, 236, 17]], [('00110011', 7), [51, 0, 236, 17, 236, 17, 236]], [('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]], [('0110000101010010', 7), [97, 82, 0, 236, 17, 236, 17]]], [[('10110100010010101010', 6), [180, 74, 160, 236, 17, 236]], [('011101111111100011100011', 7), [119, 248, 227, 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]]], [[('1111000000101011111011101010001011', 7), [240, 43, 238, 162, 192, 236, 17]], [('00110110', 5), [54, 0, 236, 17, 236]], [('101000101010111110000010011010001', 4), None], [('010001110011110110111010011110011100001110101010100001101', 7), None], [('1001000010101110001111000110111010001110', 5), [144, 174, 60, 110, 142]], [('000101100', 1), None], [('10111101', 1), [189]], [('', 2), [0, 236]]]]
labels = ["regression: pad alternation index", "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 alternation index 0	`[14, 87, 180, 111, 0, 236]`	`[14, 87, 180, 111, 0, 236]`	Passed
repair trap 1	`[125, 91, 232, 0, 17]`	`[125, 91, 232, 0, 236]`	Failed
combined fault 2	`[240]`	`[240]`	Passed
control 3	`[190, 19, 91, 118]`	`[190, 19, 91, 118]`	Passed
control 4	`[72, 166, 50, 160]`	`[72, 166, 50, 160]`	Passed
boundary 5	`[117]`	`[117]`	Passed
boundary 6	`[89, 232]`	`[89, 232]`	Passed
control 7	`[124, 253, 0, 236, 17]`	`[124, 253, 0, 236, 17]`	Passed

SHA-256 / 1783eea3d14e216f3b4bcab8ecc9d1a3b05adee3c8db724e002cf0363af89859

3 / The verified repair

Exit 0

"""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, 0x11]
    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 = [[[('00001110010101111011010001101111', 6), [14, 87, 180, 111, 0, 236]], [('011111010101101111101000', 5), [125, 91, 232, 0, 236]], [('11110', 1), [240]], [('10111110000100110101101101110110', 4), [190, 19, 91, 118]], [('010010001010011000110010101000', 4), [72, 166, 50, 160]], [('01110101', 1), [117]], [('0101100111101', 2), [89, 232]], [('0111110011111101', 5), [124, 253, 0, 236, 17]]], [[('010111100111111111101011010111010101', 6), [94, 127, 235, 93, 80, 236]], [('1001000010010100100111111101111110011111', 7), [144, 148, 159, 223, 159, 0, 236]], [('10110001011011111', 2), None], [('00101010101111110010011110110010011100110', 5), None], [('000100', 1), [16]], [('0110001100001', 2), [99, 8]], [('1110111100000001110000101', 3), None], [('', 4), [0, 236, 17, 236]]], [[('', 3), [0, 236, 17]], [('00110011', 7), [51, 0, 236, 17, 236, 17, 236]], [('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]], [('0110000101010010', 7), [97, 82, 0, 236, 17, 236, 17]]], [[('10110100010010101010', 6), [180, 74, 160, 236, 17, 236]], [('011101111111100011100011', 7), [119, 248, 227, 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]]], [[('1111000000101011111011101010001011', 7), [240, 43, 238, 162, 192, 236, 17]], [('00110110', 5), [54, 0, 236, 17, 236]], [('101000101010111110000010011010001', 4), None], [('010001110011110110111010011110011100001110101010100001101', 7), None], [('1001000010101110001111000110111010001110', 5), [144, 174, 60, 110, 142]], [('000101100', 1), None], [('10111101', 1), [189]], [('', 2), [0, 236]]]]
labels = ["regression: pad alternation index", "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 alternation index 0	`[14, 87, 180, 111, 0, 236]`	`[14, 87, 180, 111, 0, 236]`	Passed
repair trap 1	`[125, 91, 232, 0, 236]`	`[125, 91, 232, 0, 236]`	Passed
combined fault 2	`[240]`	`[240]`	Passed
control 3	`[190, 19, 91, 118]`	`[190, 19, 91, 118]`	Passed
control 4	`[72, 166, 50, 160]`	`[72, 166, 50, 160]`	Passed
boundary 5	`[117]`	`[117]`	Passed
boundary 6	`[89, 232]`	`[89, 232]`	Passed
control 7	`[124, 253, 0, 236, 17]`	`[124, 253, 0, 236, 17]`	Passed

SHA-256 / cea271e31cfa5fe3a4c77c69d8f726422be0ebdc634778c6b85fd22e02bb438c

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 2026-09-29T14:49:46.986999+00:00.

Case digest / a3fca129fac97fe17619daa847247e1de259a7eaacc8dd94afb3801475ac5038