FAILURE MAP
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FA-78056 / Subtitle cue timing / Open access

Overlapping cue row stacking: row occupancy update · case 01

Reused rows receive overlapping cues.

Verified by executionVariant 1 · 9 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

Reusing a row records the cue start as its occupancy end.

VERIFIED REPAIR

Record the cue end as the row occupancy end.

Unsuccessful approach: Recording e-1 frees the row one millisecond before the cue actually ends.

Case contract

Assign overlapping cues to display rows. Cues are processed by (start, input index); a row is free at time t when the last cue placed there ended at or before t (end exclusive). Each cue takes the lowest free row, or opens a new row. Return the row of each cue in input order.

Why this case matters

Subtitle timing defects shift, hide or overlap captions that viewers depend on for comprehension and accessibility.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(cues):
    order=sorted(range(len(cues)),key=lambda i:(cues[i][0],i))
    row_end=[]
    rows=[0]*len(cues)
    for i in order:
        s,e=cues[i]
        for r in range(len(row_end)):
            if row_end[r]<=s:
                row_end[r]=s
                rows[i]=r
                break
        else:
            row_end.append(e)
            rows[i]=len(row_end)-1
    return rows
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: row occupancy update', [[[500, 700], [200, 400], [300, 400], [500, 900], [300, 500], [1000, 1400]]], [0, 0, 1, 1, 2, 0]), ('regression variant: row occupancy update', [[[500, 800], [300, 400], [1000, 1200], [300, 600], [500, 600], [200, 300]]], [0, 0, 0, 1, 2, 0]), ('partial repair probe: row occupancy update', [[[1000, 1400], [300, 700], [1000, 1001], [0, 400], [300, 1200], [1000, 1100]]], [0, 1, 1, 0, 2, 3]), ('partial repair variant: row occupancy update', [[[500, 501], [300, 301], [1000, 1300], [300, 500], [0, 400], [100, 101]]], [0, 1, 0, 2, 0, 1]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('normal control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('normal control', [[[1000, 1001]]], [0]), ('normal control', [[[100, 1000]]], [0])], [('regression: row occupancy update', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('regression variant: row occupancy update', [[[1000, 1900], [200, 500], [0, 1], [100, 500], [500, 700], [0, 900]]], [0, 2, 0, 0, 0, 1]), ('partial repair probe: row occupancy update', [[[1000, 1001], [0, 300], [300, 600], [1000, 1001], [0, 1], [0, 900]]], [0, 0, 0, 1, 1, 2]), ('partial repair variant: row occupancy update', [[[100, 101], [200, 201], [1000, 1300], [1000, 1100], [0, 200], [200, 201]]], [1, 0, 0, 1, 0, 1]), ('boundary control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('normal control', [[[200, 500], [0, 900], [1000, 1100], [500, 900]]], [1, 0, 0, 1]), ('normal control', [[[200, 300], [500, 900]]], [0, 0]), ('normal control', [[[0, 400], [300, 600]]], [0, 1])], [('regression: row occupancy update', [[[100, 1000], [500, 600], [200, 500], [200, 500], [0, 200], [100, 101]]], [1, 0, 0, 2, 0, 2]), ('regression variant: row occupancy update', [[[200, 201], [300, 500], [200, 201], [1000, 1300], [300, 400], [0, 200]]], [0, 0, 1, 0, 1, 0]), ('partial repair probe: row occupancy update', [[[0, 200], [300, 700], [1000, 1001], [1000, 1001], [100, 300]]], [0, 0, 0, 1, 1]), ('partial repair variant: row occupancy update', [[[500, 501], [500, 1400], [1000, 1400], [1000, 1300], [0, 200]]], [0, 1, 0, 2, 0]), ('boundary control', [[[0, 100]]], [0]), ('boundary control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('normal control', [[[100, 500], [200, 300], [1000, 1100]]], [0, 1, 0]), ('normal control', [[[300, 500], [100, 500]]], [1, 0]), ('normal control', [[[300, 700]]], [0])], [('regression: row occupancy update', [[[0, 300], [500, 1400], [0, 1], [1000, 1001], [1000, 1300]]], [0, 0, 1, 1, 2]), ('regression variant: row occupancy update', [[[200, 500], [1000, 1100], [1000, 1200], [0, 900]]], [1, 0, 1, 0]), ('partial repair probe: row occupancy update', [[[300, 400], [1000, 1001], [1000, 1900]]], [0, 0, 1]), ('partial repair variant: row occupancy update', [[[0, 300], [0, 1], [200, 201], [200, 400], [200, 500]]], [0, 1, 1, 2, 3]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[100, 200], [500, 501]]], [0, 0]), ('normal control', [[[0, 900], [200, 1100]]], [0, 1]), ('normal control', [[[300, 1200], [0, 200]]], [0, 0])], [('regression: row occupancy update', [[[300, 1200], [1000, 1100], [300, 301], [100, 101]]], [0, 1, 1, 0]), ('regression variant: row occupancy update', [[[1000, 1900], [100, 200], [500, 501], [200, 1100], [300, 301]]], [1, 0, 1, 0, 1]), ('partial repair probe: row occupancy update', [[[200, 300], [500, 600], [1000, 1001], [1000, 1300], [100, 1000], [0, 300]]], [2, 0, 0, 1, 1, 0]), ('partial repair variant: row occupancy update', [[[1000, 1300], [0, 900], [200, 300], [300, 301], [300, 1200]]], [0, 0, 1, 1, 2]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('normal control', [[[0, 900], [500, 1400]]], [0, 1]), ('normal control', [[[300, 301], [0, 900], [0, 900], [0, 400], [0, 300]]], [3, 0, 1, 2, 3]), ('normal control', [[[300, 600]]], [0])]]
for label, args, expected in fixtures[N-1]:
    check(label, 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 fixtureActualExpectedOutcome
regression: row occupancy update[0, 0, 1, 0, 2, 0][0, 0, 1, 1, 2, 0]Failed
regression variant: row occupancy update[0, 0, 0, 0, 0, 0][0, 0, 0, 1, 2, 0]Failed
partial repair probe: row occupancy update[0, 1, 0, 0, 2, 0][0, 1, 1, 0, 2, 3]Failed
partial repair variant: row occupancy update[0, 1, 0, 1, 0, 1][0, 1, 0, 2, 0, 1]Failed
boundary control[0, 0][0, 0]Passed
boundary control[0, 1, 1][0, 1, 1]Passed
normal control[0, 1, 1][0, 1, 1]Passed
normal control[0][0]Passed
normal control[0][0]Passed

SHA-256 / 09692c319481f670da0cc3052c05423bd55849137397b622be75c0767cf80c1b

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(cues):
    order=sorted(range(len(cues)),key=lambda i:(cues[i][0],i))
    row_end=[]
    rows=[0]*len(cues)
    for i in order:
        s,e=cues[i]
        for r in range(len(row_end)):
            if row_end[r]<=s:
                row_end[r]=e-1
                rows[i]=r
                break
        else:
            row_end.append(e)
            rows[i]=len(row_end)-1
    return rows
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: row occupancy update', [[[500, 700], [200, 400], [300, 400], [500, 900], [300, 500], [1000, 1400]]], [0, 0, 1, 1, 2, 0]), ('regression variant: row occupancy update', [[[500, 800], [300, 400], [1000, 1200], [300, 600], [500, 600], [200, 300]]], [0, 0, 0, 1, 2, 0]), ('partial repair probe: row occupancy update', [[[1000, 1400], [300, 700], [1000, 1001], [0, 400], [300, 1200], [1000, 1100]]], [0, 1, 1, 0, 2, 3]), ('partial repair variant: row occupancy update', [[[500, 501], [300, 301], [1000, 1300], [300, 500], [0, 400], [100, 101]]], [0, 1, 0, 2, 0, 1]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('normal control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('normal control', [[[1000, 1001]]], [0]), ('normal control', [[[100, 1000]]], [0])], [('regression: row occupancy update', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('regression variant: row occupancy update', [[[1000, 1900], [200, 500], [0, 1], [100, 500], [500, 700], [0, 900]]], [0, 2, 0, 0, 0, 1]), ('partial repair probe: row occupancy update', [[[1000, 1001], [0, 300], [300, 600], [1000, 1001], [0, 1], [0, 900]]], [0, 0, 0, 1, 1, 2]), ('partial repair variant: row occupancy update', [[[100, 101], [200, 201], [1000, 1300], [1000, 1100], [0, 200], [200, 201]]], [1, 0, 0, 1, 0, 1]), ('boundary control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('normal control', [[[200, 500], [0, 900], [1000, 1100], [500, 900]]], [1, 0, 0, 1]), ('normal control', [[[200, 300], [500, 900]]], [0, 0]), ('normal control', [[[0, 400], [300, 600]]], [0, 1])], [('regression: row occupancy update', [[[100, 1000], [500, 600], [200, 500], [200, 500], [0, 200], [100, 101]]], [1, 0, 0, 2, 0, 2]), ('regression variant: row occupancy update', [[[200, 201], [300, 500], [200, 201], [1000, 1300], [300, 400], [0, 200]]], [0, 0, 1, 0, 1, 0]), ('partial repair probe: row occupancy update', [[[0, 200], [300, 700], [1000, 1001], [1000, 1001], [100, 300]]], [0, 0, 0, 1, 1]), ('partial repair variant: row occupancy update', [[[500, 501], [500, 1400], [1000, 1400], [1000, 1300], [0, 200]]], [0, 1, 0, 2, 0]), ('boundary control', [[[0, 100]]], [0]), ('boundary control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('normal control', [[[100, 500], [200, 300], [1000, 1100]]], [0, 1, 0]), ('normal control', [[[300, 500], [100, 500]]], [1, 0]), ('normal control', [[[300, 700]]], [0])], [('regression: row occupancy update', [[[0, 300], [500, 1400], [0, 1], [1000, 1001], [1000, 1300]]], [0, 0, 1, 1, 2]), ('regression variant: row occupancy update', [[[200, 500], [1000, 1100], [1000, 1200], [0, 900]]], [1, 0, 1, 0]), ('partial repair probe: row occupancy update', [[[300, 400], [1000, 1001], [1000, 1900]]], [0, 0, 1]), ('partial repair variant: row occupancy update', [[[0, 300], [0, 1], [200, 201], [200, 400], [200, 500]]], [0, 1, 1, 2, 3]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[100, 200], [500, 501]]], [0, 0]), ('normal control', [[[0, 900], [200, 1100]]], [0, 1]), ('normal control', [[[300, 1200], [0, 200]]], [0, 0])], [('regression: row occupancy update', [[[300, 1200], [1000, 1100], [300, 301], [100, 101]]], [0, 1, 1, 0]), ('regression variant: row occupancy update', [[[1000, 1900], [100, 200], [500, 501], [200, 1100], [300, 301]]], [1, 0, 1, 0, 1]), ('partial repair probe: row occupancy update', [[[200, 300], [500, 600], [1000, 1001], [1000, 1300], [100, 1000], [0, 300]]], [2, 0, 0, 1, 1, 0]), ('partial repair variant: row occupancy update', [[[1000, 1300], [0, 900], [200, 300], [300, 301], [300, 1200]]], [0, 0, 1, 1, 2]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('normal control', [[[0, 900], [500, 1400]]], [0, 1]), ('normal control', [[[300, 301], [0, 900], [0, 900], [0, 400], [0, 300]]], [3, 0, 1, 2, 3]), ('normal control', [[[300, 600]]], [0])]]
for label, args, expected in fixtures[N-1]:
    check(label, 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 fixtureActualExpectedOutcome
regression: row occupancy update[0, 0, 1, 1, 2, 0][0, 0, 1, 1, 2, 0]Passed
regression variant: row occupancy update[0, 0, 0, 1, 2, 0][0, 0, 0, 1, 2, 0]Passed
partial repair probe: row occupancy update[0, 1, 1, 0, 2, 1][0, 1, 1, 0, 2, 3]Failed
partial repair variant: row occupancy update[0, 1, 0, 1, 0, 1][0, 1, 0, 2, 0, 1]Failed
boundary control[0, 0][0, 0]Passed
boundary control[0, 1, 1][0, 1, 1]Passed
normal control[0, 1, 1][0, 1, 1]Passed
normal control[0][0]Passed
normal control[0][0]Passed

SHA-256 / 6c46f1aae21ede5cbd782f5f7f1b395734886ae8103c76e08de6d514093da70c

3 / The verified repair

Exit 0
"""Failure Map reference implementation. Python standard library only."""
import json

N = 1
observations = []
def solve(cues):
    order=sorted(range(len(cues)),key=lambda i:(cues[i][0],i))
    row_end=[]
    rows=[0]*len(cues)
    for i in order:
        s,e=cues[i]
        for r in range(len(row_end)):
            if row_end[r]<=s:
                row_end[r]=e
                rows[i]=r
                break
        else:
            row_end.append(e)
            rows[i]=len(row_end)-1
    return rows
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: row occupancy update', [[[500, 700], [200, 400], [300, 400], [500, 900], [300, 500], [1000, 1400]]], [0, 0, 1, 1, 2, 0]), ('regression variant: row occupancy update', [[[500, 800], [300, 400], [1000, 1200], [300, 600], [500, 600], [200, 300]]], [0, 0, 0, 1, 2, 0]), ('partial repair probe: row occupancy update', [[[1000, 1400], [300, 700], [1000, 1001], [0, 400], [300, 1200], [1000, 1100]]], [0, 1, 1, 0, 2, 3]), ('partial repair variant: row occupancy update', [[[500, 501], [300, 301], [1000, 1300], [300, 500], [0, 400], [100, 101]]], [0, 1, 0, 2, 0, 1]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('normal control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('normal control', [[[1000, 1001]]], [0]), ('normal control', [[[100, 1000]]], [0])], [('regression: row occupancy update', [[[1000, 1900], [100, 1000], [500, 900], [500, 501], [500, 900], [200, 300]]], [0, 0, 1, 2, 3, 1]), ('regression variant: row occupancy update', [[[1000, 1900], [200, 500], [0, 1], [100, 500], [500, 700], [0, 900]]], [0, 2, 0, 0, 0, 1]), ('partial repair probe: row occupancy update', [[[1000, 1001], [0, 300], [300, 600], [1000, 1001], [0, 1], [0, 900]]], [0, 0, 0, 1, 1, 2]), ('partial repair variant: row occupancy update', [[[100, 101], [200, 201], [1000, 1300], [1000, 1100], [0, 200], [200, 201]]], [1, 0, 0, 1, 0, 1]), ('boundary control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('normal control', [[[200, 500], [0, 900], [1000, 1100], [500, 900]]], [1, 0, 0, 1]), ('normal control', [[[200, 300], [500, 900]]], [0, 0]), ('normal control', [[[0, 400], [300, 600]]], [0, 1])], [('regression: row occupancy update', [[[100, 1000], [500, 600], [200, 500], [200, 500], [0, 200], [100, 101]]], [1, 0, 0, 2, 0, 2]), ('regression variant: row occupancy update', [[[200, 201], [300, 500], [200, 201], [1000, 1300], [300, 400], [0, 200]]], [0, 0, 1, 0, 1, 0]), ('partial repair probe: row occupancy update', [[[0, 200], [300, 700], [1000, 1001], [1000, 1001], [100, 300]]], [0, 0, 0, 1, 1]), ('partial repair variant: row occupancy update', [[[500, 501], [500, 1400], [1000, 1400], [1000, 1300], [0, 200]]], [0, 1, 0, 2, 0]), ('boundary control', [[[0, 100]]], [0]), ('boundary control', [[[0, 300], [100, 200], [200, 400]]], [0, 1, 1]), ('normal control', [[[100, 500], [200, 300], [1000, 1100]]], [0, 1, 0]), ('normal control', [[[300, 500], [100, 500]]], [1, 0]), ('normal control', [[[300, 700]]], [0])], [('regression: row occupancy update', [[[0, 300], [500, 1400], [0, 1], [1000, 1001], [1000, 1300]]], [0, 0, 1, 1, 2]), ('regression variant: row occupancy update', [[[200, 500], [1000, 1100], [1000, 1200], [0, 900]]], [1, 0, 1, 0]), ('partial repair probe: row occupancy update', [[[300, 400], [1000, 1001], [1000, 1900]]], [0, 0, 1]), ('partial repair variant: row occupancy update', [[[0, 300], [0, 1], [200, 201], [200, 400], [200, 500]]], [0, 1, 1, 2, 3]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('boundary control', [[[0, 100]]], [0]), ('normal control', [[[100, 200], [500, 501]]], [0, 0]), ('normal control', [[[0, 900], [200, 1100]]], [0, 1]), ('normal control', [[[300, 1200], [0, 200]]], [0, 0])], [('regression: row occupancy update', [[[300, 1200], [1000, 1100], [300, 301], [100, 101]]], [0, 1, 1, 0]), ('regression variant: row occupancy update', [[[1000, 1900], [100, 200], [500, 501], [200, 1100], [300, 301]]], [1, 0, 1, 0, 1]), ('partial repair probe: row occupancy update', [[[200, 300], [500, 600], [1000, 1001], [1000, 1300], [100, 1000], [0, 300]]], [2, 0, 0, 1, 1, 0]), ('partial repair variant: row occupancy update', [[[1000, 1300], [0, 900], [200, 300], [300, 301], [300, 1200]]], [0, 0, 1, 1, 2]), ('boundary control', [[[0, 100], [100, 200]]], [0, 0]), ('boundary control', [[[0, 500], [0, 100], [100, 200]]], [0, 1, 1]), ('normal control', [[[0, 900], [500, 1400]]], [0, 1]), ('normal control', [[[300, 301], [0, 900], [0, 900], [0, 400], [0, 300]]], [3, 0, 1, 2, 3]), ('normal control', [[[300, 600]]], [0])]]
for label, args, expected in fixtures[N-1]:
    check(label, 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 fixtureActualExpectedOutcome
regression: row occupancy update[0, 0, 1, 1, 2, 0][0, 0, 1, 1, 2, 0]Passed
regression variant: row occupancy update[0, 0, 0, 1, 2, 0][0, 0, 0, 1, 2, 0]Passed
partial repair probe: row occupancy update[0, 1, 1, 0, 2, 3][0, 1, 1, 0, 2, 3]Passed
partial repair variant: row occupancy update[0, 1, 0, 2, 0, 1][0, 1, 0, 2, 0, 1]Passed
boundary control[0, 0][0, 0]Passed
boundary control[0, 1, 1][0, 1, 1]Passed
normal control[0, 1, 1][0, 1, 1]Passed
normal control[0][0]Passed
normal control[0][0]Passed

SHA-256 / 6fbad1d082a1752150438dd293bdb47cc12a866e8f1b41ccceca490061219956

Verification & scope

A deterministic bounded teaching model with a stipulated toy contract; it does not claim conformance to any subtitle standard. 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 2026-09-29T14:49:31.749248+00:00.

Case digest / 00f98dda02877c08a7ecf766e9d5885293c68b54838e2b9b04ef2dd79c54a957