FAILURE MAP
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FA-36821 / Tab interfaces / Open access

Multirow tab strip: active logical row · case 01

The tab workspace reports an incorrect active logical row.

Verified by executionVariant 1 · 6 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The active logical row decision uses x['active_index'] instead of next(i for i,row in enumerate(pack(x['widths'],x['available'])) if x['active_index'] in row).

THE FAILURE

The active logical row decision uses x['active_index'] instead of next(i for i,row in enumerate(pack(x['widths'],x['available'])) if x['active_index'] in row).

Unsuccessful approach: The partial repair 0 still violates a workspace boundary or normal case.

Case contract

A multirow strip greedily packs indivisible tab widths with exact fits allowed. The active tab row is painted adjacent to content after other rows; tab identity and within-row coordinates follow this row permutation.

Why this case matters

Offline tab/panel workspace behavior; no browser or desktop framework is emulated.

1 / The failure

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
def pack(widths, available):
    rows = [[]]
    occupied = 0
    for index, width in enumerate(widths):
        if rows[-1] and occupied + width > available:
            rows.append([])
            occupied = 0
        rows[-1].append(index)
        occupied += width
    return rows

N = 1
observations = []
def solve(x):
    r0 = pack(x['widths'],x['available'])
    r1 = sum(x['widths'][j] for j in next(row for row in pack(x['widths'],x['available']) if x['active_index'] in row) if j<x['active_index'])
    r2 = x['active_index']
    r3 = [row for row in pack(x['widths'],x['available']) if x['active_index'] not in row]+[row for row in pack(x['widths'],x['available']) if x['active_index'] in row]
    r4 = (len(pack(x['widths'],x['available']))-1)*x['row_height']
    r5 = [t for row in ([row for row in pack(x['widths'],x['available']) if x['active_index'] not in row]+[row for row in pack(x['widths'],x['available']) if x['active_index'] in row]) for t in row]
    return [r0,r1,r2,r3,r4,r5]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = {1: [({'widths': [80, 70, 90, 30], 'available': 160, 'active_index': 2, 'row_height': 24}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]]), ({'widths': [80, 80, 50, 100], 'available': 160, 'active_index': 2, 'row_height': 24}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]]), ({'widths': [80, 70, 90, 30], 'available': 160, 'active_index': 0, 'row_height': 24}, [[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 24, [2, 3, 0, 1]]), ({'widths': [80, 70, 90, 30], 'available': 160, 'active_index': 3, 'row_height': 24}, [[[0, 1], [2, 3]], 90, 1, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]]), ({'widths': [80, 70, 90, 30], 'available': 300, 'active_index': 2, 'row_height': 24}, [[[0, 1, 2, 3]], 150, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]]), ({'widths': [100, 70, 70, 90], 'available': 160, 'active_index': 2, 'row_height': 24}, [[[0], [1, 2], [3]], 70, 1, [[0], [3], [1, 2]], 48, [0, 3, 1, 2]])], 2: [({'widths': [160, 140, 180, 60], 'available': 320, 'active_index': 2, 'row_height': 48}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 48, [0, 1, 2, 3]]), ({'widths': [160, 160, 100, 200], 'available': 320, 'active_index': 2, 'row_height': 48}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 48, [0, 1, 2, 3]]), ({'widths': [160, 140, 180, 60], 'available': 320, 'active_index': 0, 'row_height': 48}, [[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 48, [2, 3, 0, 1]]), ({'widths': [160, 140, 180, 60], 'available': 320, 'active_index': 3, 'row_height': 48}, [[[0, 1], [2, 3]], 180, 1, [[0, 1], [2, 3]], 48, [0, 1, 2, 3]]), ({'widths': [160, 140, 180, 60], 'available': 600, 'active_index': 2, 'row_height': 48}, [[[0, 1, 2, 3]], 300, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]]), ({'widths': [200, 140, 140, 180], 'available': 320, 'active_index': 2, 'row_height': 48}, [[[0], [1, 2], [3]], 140, 1, [[0], [3], [1, 2]], 96, [0, 3, 1, 2]])], 3: [({'widths': [240, 210, 270, 90], 'available': 480, 'active_index': 2, 'row_height': 72}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 72, [0, 1, 2, 3]]), ({'widths': [240, 240, 150, 300], 'available': 480, 'active_index': 2, 'row_height': 72}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 72, [0, 1, 2, 3]]), ({'widths': [240, 210, 270, 90], 'available': 480, 'active_index': 0, 'row_height': 72}, [[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 72, [2, 3, 0, 1]]), ({'widths': [240, 210, 270, 90], 'available': 480, 'active_index': 3, 'row_height': 72}, [[[0, 1], [2, 3]], 270, 1, [[0, 1], [2, 3]], 72, [0, 1, 2, 3]]), ({'widths': [240, 210, 270, 90], 'available': 900, 'active_index': 2, 'row_height': 72}, [[[0, 1, 2, 3]], 450, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]]), ({'widths': [300, 210, 210, 270], 'available': 480, 'active_index': 2, 'row_height': 72}, [[[0], [1, 2], [3]], 210, 1, [[0], [3], [1, 2]], 144, [0, 3, 1, 2]])], 4: [({'widths': [320, 280, 360, 120], 'available': 640, 'active_index': 2, 'row_height': 96}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 96, [0, 1, 2, 3]]), ({'widths': [320, 320, 200, 400], 'available': 640, 'active_index': 2, 'row_height': 96}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 96, [0, 1, 2, 3]]), ({'widths': [320, 280, 360, 120], 'available': 640, 'active_index': 0, 'row_height': 96}, [[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 96, [2, 3, 0, 1]]), ({'widths': [320, 280, 360, 120], 'available': 640, 'active_index': 3, 'row_height': 96}, [[[0, 1], [2, 3]], 360, 1, [[0, 1], [2, 3]], 96, [0, 1, 2, 3]]), ({'widths': [320, 280, 360, 120], 'available': 1200, 'active_index': 2, 'row_height': 96}, [[[0, 1, 2, 3]], 600, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]]), ({'widths': [400, 280, 280, 360], 'available': 640, 'active_index': 2, 'row_height': 96}, [[[0], [1, 2], [3]], 280, 1, [[0], [3], [1, 2]], 192, [0, 3, 1, 2]])], 5: [({'widths': [400, 350, 450, 150], 'available': 800, 'active_index': 2, 'row_height': 120}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 120, [0, 1, 2, 3]]), ({'widths': [400, 400, 250, 500], 'available': 800, 'active_index': 2, 'row_height': 120}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 120, [0, 1, 2, 3]]), ({'widths': [400, 350, 450, 150], 'available': 800, 'active_index': 0, 'row_height': 120}, [[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 120, [2, 3, 0, 1]]), ({'widths': [400, 350, 450, 150], 'available': 800, 'active_index': 3, 'row_height': 120}, [[[0, 1], [2, 3]], 450, 1, [[0, 1], [2, 3]], 120, [0, 1, 2, 3]]), ({'widths': [400, 350, 450, 150], 'available': 1500, 'active_index': 2, 'row_height': 120}, [[[0, 1, 2, 3]], 750, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]]), ({'widths': [500, 350, 350, 450], 'available': 800, 'active_index': 2, 'row_height': 120}, [[[0], [1, 2], [3]], 350, 1, [[0], [3], [1, 2]], 240, [0, 3, 1, 2]])]}
for i, (inputs, expected) in enumerate(fixtures[N]):
    check("workspace regression "+str(i), solve(inputs), 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
workspace regression 0[[[0, 1], [2, 3]], 0, 2, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]][[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]]Failed
workspace regression 1[[[0, 1], [2, 3]], 0, 2, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]][[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]]Failed
workspace regression 2[[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 24, [2, 3, 0, 1]][[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 24, [2, 3, 0, 1]]Passed
workspace regression 3[[[0, 1], [2, 3]], 90, 3, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]][[[0, 1], [2, 3]], 90, 1, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]]Failed
workspace regression 4[[[0, 1, 2, 3]], 150, 2, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]][[[0, 1, 2, 3]], 150, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]]Failed
workspace regression 5[[[0], [1, 2], [3]], 70, 2, [[0], [3], [1, 2]], 48, [0, 3, 1, 2]][[[0], [1, 2], [3]], 70, 1, [[0], [3], [1, 2]], 48, [0, 3, 1, 2]]Failed

SHA-256 / 1825b5d4669fd0e500e3291501b61a95dd0d6cc055954e2e3a3eaf40c92e036b

2 / The unsuccessful fix

Exit 1
"""Failure Map reference implementation. Python standard library only."""
import json
def pack(widths, available):
    rows = [[]]
    occupied = 0
    for index, width in enumerate(widths):
        if rows[-1] and occupied + width > available:
            rows.append([])
            occupied = 0
        rows[-1].append(index)
        occupied += width
    return rows

N = 1
observations = []
def solve(x):
    r0 = pack(x['widths'],x['available'])
    r1 = sum(x['widths'][j] for j in next(row for row in pack(x['widths'],x['available']) if x['active_index'] in row) if j<x['active_index'])
    r2 = 0
    r3 = [row for row in pack(x['widths'],x['available']) if x['active_index'] not in row]+[row for row in pack(x['widths'],x['available']) if x['active_index'] in row]
    r4 = (len(pack(x['widths'],x['available']))-1)*x['row_height']
    r5 = [t for row in ([row for row in pack(x['widths'],x['available']) if x['active_index'] not in row]+[row for row in pack(x['widths'],x['available']) if x['active_index'] in row]) for t in row]
    return [r0,r1,r2,r3,r4,r5]
def check(label, actual, expected):
    observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = {1: [({'widths': [80, 70, 90, 30], 'available': 160, 'active_index': 2, 'row_height': 24}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]]), ({'widths': [80, 80, 50, 100], 'available': 160, 'active_index': 2, 'row_height': 24}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]]), ({'widths': [80, 70, 90, 30], 'available': 160, 'active_index': 0, 'row_height': 24}, [[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 24, [2, 3, 0, 1]]), ({'widths': [80, 70, 90, 30], 'available': 160, 'active_index': 3, 'row_height': 24}, [[[0, 1], [2, 3]], 90, 1, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]]), ({'widths': [80, 70, 90, 30], 'available': 300, 'active_index': 2, 'row_height': 24}, [[[0, 1, 2, 3]], 150, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]]), ({'widths': [100, 70, 70, 90], 'available': 160, 'active_index': 2, 'row_height': 24}, [[[0], [1, 2], [3]], 70, 1, [[0], [3], [1, 2]], 48, [0, 3, 1, 2]])], 2: [({'widths': [160, 140, 180, 60], 'available': 320, 'active_index': 2, 'row_height': 48}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 48, [0, 1, 2, 3]]), ({'widths': [160, 160, 100, 200], 'available': 320, 'active_index': 2, 'row_height': 48}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 48, [0, 1, 2, 3]]), ({'widths': [160, 140, 180, 60], 'available': 320, 'active_index': 0, 'row_height': 48}, [[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 48, [2, 3, 0, 1]]), ({'widths': [160, 140, 180, 60], 'available': 320, 'active_index': 3, 'row_height': 48}, [[[0, 1], [2, 3]], 180, 1, [[0, 1], [2, 3]], 48, [0, 1, 2, 3]]), ({'widths': [160, 140, 180, 60], 'available': 600, 'active_index': 2, 'row_height': 48}, [[[0, 1, 2, 3]], 300, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]]), ({'widths': [200, 140, 140, 180], 'available': 320, 'active_index': 2, 'row_height': 48}, [[[0], [1, 2], [3]], 140, 1, [[0], [3], [1, 2]], 96, [0, 3, 1, 2]])], 3: [({'widths': [240, 210, 270, 90], 'available': 480, 'active_index': 2, 'row_height': 72}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 72, [0, 1, 2, 3]]), ({'widths': [240, 240, 150, 300], 'available': 480, 'active_index': 2, 'row_height': 72}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 72, [0, 1, 2, 3]]), ({'widths': [240, 210, 270, 90], 'available': 480, 'active_index': 0, 'row_height': 72}, [[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 72, [2, 3, 0, 1]]), ({'widths': [240, 210, 270, 90], 'available': 480, 'active_index': 3, 'row_height': 72}, [[[0, 1], [2, 3]], 270, 1, [[0, 1], [2, 3]], 72, [0, 1, 2, 3]]), ({'widths': [240, 210, 270, 90], 'available': 900, 'active_index': 2, 'row_height': 72}, [[[0, 1, 2, 3]], 450, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]]), ({'widths': [300, 210, 210, 270], 'available': 480, 'active_index': 2, 'row_height': 72}, [[[0], [1, 2], [3]], 210, 1, [[0], [3], [1, 2]], 144, [0, 3, 1, 2]])], 4: [({'widths': [320, 280, 360, 120], 'available': 640, 'active_index': 2, 'row_height': 96}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 96, [0, 1, 2, 3]]), ({'widths': [320, 320, 200, 400], 'available': 640, 'active_index': 2, 'row_height': 96}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 96, [0, 1, 2, 3]]), ({'widths': [320, 280, 360, 120], 'available': 640, 'active_index': 0, 'row_height': 96}, [[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 96, [2, 3, 0, 1]]), ({'widths': [320, 280, 360, 120], 'available': 640, 'active_index': 3, 'row_height': 96}, [[[0, 1], [2, 3]], 360, 1, [[0, 1], [2, 3]], 96, [0, 1, 2, 3]]), ({'widths': [320, 280, 360, 120], 'available': 1200, 'active_index': 2, 'row_height': 96}, [[[0, 1, 2, 3]], 600, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]]), ({'widths': [400, 280, 280, 360], 'available': 640, 'active_index': 2, 'row_height': 96}, [[[0], [1, 2], [3]], 280, 1, [[0], [3], [1, 2]], 192, [0, 3, 1, 2]])], 5: [({'widths': [400, 350, 450, 150], 'available': 800, 'active_index': 2, 'row_height': 120}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 120, [0, 1, 2, 3]]), ({'widths': [400, 400, 250, 500], 'available': 800, 'active_index': 2, 'row_height': 120}, [[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 120, [0, 1, 2, 3]]), ({'widths': [400, 350, 450, 150], 'available': 800, 'active_index': 0, 'row_height': 120}, [[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 120, [2, 3, 0, 1]]), ({'widths': [400, 350, 450, 150], 'available': 800, 'active_index': 3, 'row_height': 120}, [[[0, 1], [2, 3]], 450, 1, [[0, 1], [2, 3]], 120, [0, 1, 2, 3]]), ({'widths': [400, 350, 450, 150], 'available': 1500, 'active_index': 2, 'row_height': 120}, [[[0, 1, 2, 3]], 750, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]]), ({'widths': [500, 350, 350, 450], 'available': 800, 'active_index': 2, 'row_height': 120}, [[[0], [1, 2], [3]], 350, 1, [[0], [3], [1, 2]], 240, [0, 3, 1, 2]])]}
for i, (inputs, expected) in enumerate(fixtures[N]):
    check("workspace regression "+str(i), solve(inputs), 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
workspace regression 0[[[0, 1], [2, 3]], 0, 0, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]][[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]]Failed
workspace regression 1[[[0, 1], [2, 3]], 0, 0, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]][[[0, 1], [2, 3]], 0, 1, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]]Failed
workspace regression 2[[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 24, [2, 3, 0, 1]][[[0, 1], [2, 3]], 0, 0, [[2, 3], [0, 1]], 24, [2, 3, 0, 1]]Passed
workspace regression 3[[[0, 1], [2, 3]], 90, 0, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]][[[0, 1], [2, 3]], 90, 1, [[0, 1], [2, 3]], 24, [0, 1, 2, 3]]Failed
workspace regression 4[[[0, 1, 2, 3]], 150, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]][[[0, 1, 2, 3]], 150, 0, [[0, 1, 2, 3]], 0, [0, 1, 2, 3]]Passed
workspace regression 5[[[0], [1, 2], [3]], 70, 0, [[0], [3], [1, 2]], 48, [0, 3, 1, 2]][[[0], [1, 2], [3]], 70, 1, [[0], [3], [1, 2]], 48, [0, 3, 1, 2]]Failed

SHA-256 / 5b8bb5d05b05b64eef62e896497d9871bf6851b4d8f0bb34b5c000eea98cf48f

HELD IN THE MEMBER ARCHIVE

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

This mechanism has 6 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.

Member access is invitation-based. Sign in with your invited account to inspect the repair.

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Verification & scope

Finite stipulated workspace snapshots only. Independent result fields describe observable obligations, not a full UI runtime. 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 2026-09-29T14:42:55.311092+00:00.

Case digest / 6f8260a798df868251f92b242718b6dc6dd3b3d5aaee82d1b3558ce0fafba89f