FA-36481 / Tab interfaces / Open access

Tab decoration measurement: icon footprint · case 01

The tab workspace reports an incorrect icon footprint.

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

ROOT CAUSE

The icon footprint decision uses x['icon']+x['gap'] instead of x['icon']+x['gap'] if x['icon_visible'] else 0.

VERIFIED REPAIR

Use the stipulated workspace rule: x['icon']+x['gap'] if x['icon_visible'] else 0.

Unsuccessful approach: The partial repair x['icon'] if x['icon_visible'] else 0 still violates a workspace boundary or normal case.

Case contract

Tab label layout reserves distinct icon, dirty marker, close button and padding footprints before truncating the title; absent decorations consume no width.

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

N = 1
observations = []
def solve(x):
    r0 = x['icon']+x['gap']
    r1 = x['dirty_width']+x['gap'] if x['dirty'] else 0
    r2 = x['close_width']+x['gap'] if x['close'] else 0
    r3 = 2*x['padding']
    r4 = max(0,x['width']-2*x['padding']-(x['icon']+x['gap'] if x['icon_visible'] else 0)-(x['dirty_width']+x['gap'] if x['dirty'] else 0)-(x['close_width']+x['gap'] if x['close'] else 0))
    r5 = x['title_width']>max(0,x['width']-2*x['padding']-(x['icon']+x['gap'] if x['icon_visible'] else 0)-(x['dirty_width']+x['gap'] if x['dirty'] else 0)-(x['close_width']+x['gap'] if x['close'] else 0))
    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: [({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': True, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 4, 'title_width': 110}, [20, 10, 22, 16, 72, True]), ({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': False, 'dirty': True, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 4, 'title_width': 110}, [0, 10, 22, 16, 92, True]), ({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': False, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 4, 'title_width': 110}, [20, 0, 22, 16, 82, True]), ({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': True, 'dirty_width': 6, 'close': False, 'close_width': 18, 'gap': 4, 'title_width': 110}, [20, 10, 0, 16, 94, True]), ({'width': 220, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': True, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 4, 'title_width': 110}, [20, 10, 22, 16, 152, False]), ({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': True, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 0, 'title_width': 110}, [16, 6, 18, 16, 84, True])], 2: [({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': True, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 8, 'title_width': 220}, [40, 20, 44, 32, 144, True]), ({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': False, 'dirty': True, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 8, 'title_width': 220}, [0, 20, 44, 32, 184, True]), ({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': False, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 8, 'title_width': 220}, [40, 0, 44, 32, 164, True]), ({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': True, 'dirty_width': 12, 'close': False, 'close_width': 36, 'gap': 8, 'title_width': 220}, [40, 20, 0, 32, 188, True]), ({'width': 440, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': True, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 8, 'title_width': 220}, [40, 20, 44, 32, 304, False]), ({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': True, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 0, 'title_width': 220}, [32, 12, 36, 32, 168, True])], 3: [({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': True, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 12, 'title_width': 330}, [60, 30, 66, 48, 216, True]), ({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': False, 'dirty': True, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 12, 'title_width': 330}, [0, 30, 66, 48, 276, True]), ({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': False, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 12, 'title_width': 330}, [60, 0, 66, 48, 246, True]), ({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': True, 'dirty_width': 18, 'close': False, 'close_width': 54, 'gap': 12, 'title_width': 330}, [60, 30, 0, 48, 282, True]), ({'width': 660, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': True, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 12, 'title_width': 330}, [60, 30, 66, 48, 456, False]), ({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': True, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 0, 'title_width': 330}, [48, 18, 54, 48, 252, True])], 4: [({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': True, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 16, 'title_width': 440}, [80, 40, 88, 64, 288, True]), ({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': False, 'dirty': True, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 16, 'title_width': 440}, [0, 40, 88, 64, 368, True]), ({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': False, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 16, 'title_width': 440}, [80, 0, 88, 64, 328, True]), ({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': True, 'dirty_width': 24, 'close': False, 'close_width': 72, 'gap': 16, 'title_width': 440}, [80, 40, 0, 64, 376, True]), ({'width': 880, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': True, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 16, 'title_width': 440}, [80, 40, 88, 64, 608, False]), ({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': True, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 0, 'title_width': 440}, [64, 24, 72, 64, 336, True])], 5: [({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': True, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 20, 'title_width': 550}, [100, 50, 110, 80, 360, True]), ({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': False, 'dirty': True, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 20, 'title_width': 550}, [0, 50, 110, 80, 460, True]), ({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': False, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 20, 'title_width': 550}, [100, 0, 110, 80, 410, True]), ({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': True, 'dirty_width': 30, 'close': False, 'close_width': 90, 'gap': 20, 'title_width': 550}, [100, 50, 0, 80, 470, True]), ({'width': 1100, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': True, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 20, 'title_width': 550}, [100, 50, 110, 80, 760, False]), ({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': True, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 0, 'title_width': 550}, [80, 30, 90, 80, 420, True])]}
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 fixture	Actual	Expected	Outcome
workspace regression 0	`[20, 10, 22, 16, 72, True]`	`[20, 10, 22, 16, 72, True]`	Passed
workspace regression 1	`[20, 10, 22, 16, 92, True]`	`[0, 10, 22, 16, 92, True]`	Failed
workspace regression 2	`[20, 0, 22, 16, 82, True]`	`[20, 0, 22, 16, 82, True]`	Passed
workspace regression 3	`[20, 10, 0, 16, 94, True]`	`[20, 10, 0, 16, 94, True]`	Passed
workspace regression 4	`[20, 10, 22, 16, 152, False]`	`[20, 10, 22, 16, 152, False]`	Passed
workspace regression 5	`[16, 6, 18, 16, 84, True]`	`[16, 6, 18, 16, 84, True]`	Passed

SHA-256 / 2a10b164d59f5709724bf3efc364d99040facd80ff0c67e985ca979c2e4fdabe

2 / The unsuccessful fix

Exit 1

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

N = 1
observations = []
def solve(x):
    r0 = x['icon'] if x['icon_visible'] else 0
    r1 = x['dirty_width']+x['gap'] if x['dirty'] else 0
    r2 = x['close_width']+x['gap'] if x['close'] else 0
    r3 = 2*x['padding']
    r4 = max(0,x['width']-2*x['padding']-(x['icon']+x['gap'] if x['icon_visible'] else 0)-(x['dirty_width']+x['gap'] if x['dirty'] else 0)-(x['close_width']+x['gap'] if x['close'] else 0))
    r5 = x['title_width']>max(0,x['width']-2*x['padding']-(x['icon']+x['gap'] if x['icon_visible'] else 0)-(x['dirty_width']+x['gap'] if x['dirty'] else 0)-(x['close_width']+x['gap'] if x['close'] else 0))
    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: [({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': True, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 4, 'title_width': 110}, [20, 10, 22, 16, 72, True]), ({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': False, 'dirty': True, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 4, 'title_width': 110}, [0, 10, 22, 16, 92, True]), ({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': False, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 4, 'title_width': 110}, [20, 0, 22, 16, 82, True]), ({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': True, 'dirty_width': 6, 'close': False, 'close_width': 18, 'gap': 4, 'title_width': 110}, [20, 10, 0, 16, 94, True]), ({'width': 220, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': True, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 4, 'title_width': 110}, [20, 10, 22, 16, 152, False]), ({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': True, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 0, 'title_width': 110}, [16, 6, 18, 16, 84, True])], 2: [({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': True, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 8, 'title_width': 220}, [40, 20, 44, 32, 144, True]), ({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': False, 'dirty': True, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 8, 'title_width': 220}, [0, 20, 44, 32, 184, True]), ({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': False, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 8, 'title_width': 220}, [40, 0, 44, 32, 164, True]), ({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': True, 'dirty_width': 12, 'close': False, 'close_width': 36, 'gap': 8, 'title_width': 220}, [40, 20, 0, 32, 188, True]), ({'width': 440, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': True, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 8, 'title_width': 220}, [40, 20, 44, 32, 304, False]), ({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': True, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 0, 'title_width': 220}, [32, 12, 36, 32, 168, True])], 3: [({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': True, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 12, 'title_width': 330}, [60, 30, 66, 48, 216, True]), ({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': False, 'dirty': True, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 12, 'title_width': 330}, [0, 30, 66, 48, 276, True]), ({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': False, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 12, 'title_width': 330}, [60, 0, 66, 48, 246, True]), ({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': True, 'dirty_width': 18, 'close': False, 'close_width': 54, 'gap': 12, 'title_width': 330}, [60, 30, 0, 48, 282, True]), ({'width': 660, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': True, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 12, 'title_width': 330}, [60, 30, 66, 48, 456, False]), ({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': True, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 0, 'title_width': 330}, [48, 18, 54, 48, 252, True])], 4: [({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': True, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 16, 'title_width': 440}, [80, 40, 88, 64, 288, True]), ({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': False, 'dirty': True, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 16, 'title_width': 440}, [0, 40, 88, 64, 368, True]), ({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': False, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 16, 'title_width': 440}, [80, 0, 88, 64, 328, True]), ({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': True, 'dirty_width': 24, 'close': False, 'close_width': 72, 'gap': 16, 'title_width': 440}, [80, 40, 0, 64, 376, True]), ({'width': 880, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': True, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 16, 'title_width': 440}, [80, 40, 88, 64, 608, False]), ({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': True, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 0, 'title_width': 440}, [64, 24, 72, 64, 336, True])], 5: [({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': True, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 20, 'title_width': 550}, [100, 50, 110, 80, 360, True]), ({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': False, 'dirty': True, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 20, 'title_width': 550}, [0, 50, 110, 80, 460, True]), ({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': False, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 20, 'title_width': 550}, [100, 0, 110, 80, 410, True]), ({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': True, 'dirty_width': 30, 'close': False, 'close_width': 90, 'gap': 20, 'title_width': 550}, [100, 50, 0, 80, 470, True]), ({'width': 1100, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': True, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 20, 'title_width': 550}, [100, 50, 110, 80, 760, False]), ({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': True, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 0, 'title_width': 550}, [80, 30, 90, 80, 420, True])]}
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 fixture	Actual	Expected	Outcome
workspace regression 0	`[16, 10, 22, 16, 72, True]`	`[20, 10, 22, 16, 72, True]`	Failed
workspace regression 1	`[0, 10, 22, 16, 92, True]`	`[0, 10, 22, 16, 92, True]`	Passed
workspace regression 2	`[16, 0, 22, 16, 82, True]`	`[20, 0, 22, 16, 82, True]`	Failed
workspace regression 3	`[16, 10, 0, 16, 94, True]`	`[20, 10, 0, 16, 94, True]`	Failed
workspace regression 4	`[16, 10, 22, 16, 152, False]`	`[20, 10, 22, 16, 152, False]`	Failed
workspace regression 5	`[16, 6, 18, 16, 84, True]`	`[16, 6, 18, 16, 84, True]`	Passed

SHA-256 / ab39aaad28b0499842e1c7104e0ab58f27f065f02c9faaa14e588851ee4dbcc4

3 / The verified repair

Exit 0

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

N = 1
observations = []
def solve(x):
    r0 = x['icon']+x['gap'] if x['icon_visible'] else 0
    r1 = x['dirty_width']+x['gap'] if x['dirty'] else 0
    r2 = x['close_width']+x['gap'] if x['close'] else 0
    r3 = 2*x['padding']
    r4 = max(0,x['width']-2*x['padding']-(x['icon']+x['gap'] if x['icon_visible'] else 0)-(x['dirty_width']+x['gap'] if x['dirty'] else 0)-(x['close_width']+x['gap'] if x['close'] else 0))
    r5 = x['title_width']>max(0,x['width']-2*x['padding']-(x['icon']+x['gap'] if x['icon_visible'] else 0)-(x['dirty_width']+x['gap'] if x['dirty'] else 0)-(x['close_width']+x['gap'] if x['close'] else 0))
    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: [({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': True, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 4, 'title_width': 110}, [20, 10, 22, 16, 72, True]), ({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': False, 'dirty': True, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 4, 'title_width': 110}, [0, 10, 22, 16, 92, True]), ({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': False, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 4, 'title_width': 110}, [20, 0, 22, 16, 82, True]), ({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': True, 'dirty_width': 6, 'close': False, 'close_width': 18, 'gap': 4, 'title_width': 110}, [20, 10, 0, 16, 94, True]), ({'width': 220, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': True, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 4, 'title_width': 110}, [20, 10, 22, 16, 152, False]), ({'width': 140, 'padding': 8, 'icon': 16, 'icon_visible': True, 'dirty': True, 'dirty_width': 6, 'close': True, 'close_width': 18, 'gap': 0, 'title_width': 110}, [16, 6, 18, 16, 84, True])], 2: [({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': True, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 8, 'title_width': 220}, [40, 20, 44, 32, 144, True]), ({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': False, 'dirty': True, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 8, 'title_width': 220}, [0, 20, 44, 32, 184, True]), ({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': False, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 8, 'title_width': 220}, [40, 0, 44, 32, 164, True]), ({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': True, 'dirty_width': 12, 'close': False, 'close_width': 36, 'gap': 8, 'title_width': 220}, [40, 20, 0, 32, 188, True]), ({'width': 440, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': True, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 8, 'title_width': 220}, [40, 20, 44, 32, 304, False]), ({'width': 280, 'padding': 16, 'icon': 32, 'icon_visible': True, 'dirty': True, 'dirty_width': 12, 'close': True, 'close_width': 36, 'gap': 0, 'title_width': 220}, [32, 12, 36, 32, 168, True])], 3: [({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': True, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 12, 'title_width': 330}, [60, 30, 66, 48, 216, True]), ({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': False, 'dirty': True, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 12, 'title_width': 330}, [0, 30, 66, 48, 276, True]), ({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': False, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 12, 'title_width': 330}, [60, 0, 66, 48, 246, True]), ({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': True, 'dirty_width': 18, 'close': False, 'close_width': 54, 'gap': 12, 'title_width': 330}, [60, 30, 0, 48, 282, True]), ({'width': 660, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': True, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 12, 'title_width': 330}, [60, 30, 66, 48, 456, False]), ({'width': 420, 'padding': 24, 'icon': 48, 'icon_visible': True, 'dirty': True, 'dirty_width': 18, 'close': True, 'close_width': 54, 'gap': 0, 'title_width': 330}, [48, 18, 54, 48, 252, True])], 4: [({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': True, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 16, 'title_width': 440}, [80, 40, 88, 64, 288, True]), ({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': False, 'dirty': True, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 16, 'title_width': 440}, [0, 40, 88, 64, 368, True]), ({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': False, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 16, 'title_width': 440}, [80, 0, 88, 64, 328, True]), ({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': True, 'dirty_width': 24, 'close': False, 'close_width': 72, 'gap': 16, 'title_width': 440}, [80, 40, 0, 64, 376, True]), ({'width': 880, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': True, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 16, 'title_width': 440}, [80, 40, 88, 64, 608, False]), ({'width': 560, 'padding': 32, 'icon': 64, 'icon_visible': True, 'dirty': True, 'dirty_width': 24, 'close': True, 'close_width': 72, 'gap': 0, 'title_width': 440}, [64, 24, 72, 64, 336, True])], 5: [({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': True, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 20, 'title_width': 550}, [100, 50, 110, 80, 360, True]), ({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': False, 'dirty': True, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 20, 'title_width': 550}, [0, 50, 110, 80, 460, True]), ({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': False, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 20, 'title_width': 550}, [100, 0, 110, 80, 410, True]), ({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': True, 'dirty_width': 30, 'close': False, 'close_width': 90, 'gap': 20, 'title_width': 550}, [100, 50, 0, 80, 470, True]), ({'width': 1100, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': True, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 20, 'title_width': 550}, [100, 50, 110, 80, 760, False]), ({'width': 700, 'padding': 40, 'icon': 80, 'icon_visible': True, 'dirty': True, 'dirty_width': 30, 'close': True, 'close_width': 90, 'gap': 0, 'title_width': 550}, [80, 30, 90, 80, 420, True])]}
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 fixture	Actual	Expected	Outcome
workspace regression 0	`[20, 10, 22, 16, 72, True]`	`[20, 10, 22, 16, 72, True]`	Passed
workspace regression 1	`[0, 10, 22, 16, 92, True]`	`[0, 10, 22, 16, 92, True]`	Passed
workspace regression 2	`[20, 0, 22, 16, 82, True]`	`[20, 0, 22, 16, 82, True]`	Passed
workspace regression 3	`[20, 10, 0, 16, 94, True]`	`[20, 10, 0, 16, 94, True]`	Passed
workspace regression 4	`[20, 10, 22, 16, 152, False]`	`[20, 10, 22, 16, 152, False]`	Passed
workspace regression 5	`[16, 6, 18, 16, 84, True]`	`[16, 6, 18, 16, 84, True]`	Passed

SHA-256 / dffa65f4bfdf716877b5400200194940c73514e0dcc7c240c11697cbfc8c3f1e

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 2026-09-29T14:42:51.819431+00:00.

Case digest / 62191324aa484dc7f122232437b4d7859d32a38160ef51f1ac409ae76948566a