FA-93871 / Shift rostering labor rules / Open access
Seventh-day premium triggered by any Sunday shift · case 01
A worker with a normal Sunday shift after days off receives seventh-consecutive-day premiums.
ROOT CAUSE
The seventh-day rule checks only whether day 7 was worked, not whether all seven days were worked.
VERIFIED REPAIR
Apply the seventh-day rates only when every day of the workweek has worked minutes.
Unsuccessful approach: Counting six worked days still triggers the premium when a mid-week day was off.
Case contract
Seven daily worked minutes (Mon..Sun) and an hourly rate in cents. Daily: first 480 minutes regular, next 240 at 1.5x, beyond 720 at 2x. If all seven days are worked, day 7 pays its first 480 minutes at 1.5x and the rest at 2x. Regular minutes above 2400 in the week move to 1.5x (daily overtime minutes never count toward the weekly threshold). Pay is computed exactly and rounded half up to a cent once. Return [regular, ot15, ot2, pay_cents].
Why this case matters
Overtime classification mistakes are a classic payroll-roster defect: pyramiding, seventh-day rules and rounding stage all change what workers are paid.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(days, rate):
reg = ot15 = ot2 = 0
seventh = days[6] > 0
for d, m in enumerate(days):
if d == 6 and seventh:
ot15 += min(m, 480)
ot2 += max(m - 480, 0)
continue
reg += min(m, 480)
ot15 += min(max(m - 480, 0), 240)
ot2 += max(m - 720, 0)
excess = max(reg - 2400, 0)
reg -= excess
ot15 += excess
units = reg * rate * 2 + ot15 * rate * 3 + ot2 * rate * 4
pay = (units + 60) // 120
return [reg, ot15, ot2, pay]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: seventh day detection 1', [[0, 450, 480, 480, 540, 481, 540], 2000], [2400, 571, 0, 108550]),
('regression variant: seventh day detection 2', [[240, 540, 720, 240, 600, 0, 780], 90],
[2400, 660, 60, 5265]),
('partial repair guard 3', [[720, 720, 900, 0, 780, 240, 780], 1725], [2400, 1440, 300, 148350]),
('boundary control 4', [[480, 480, 480, 480, 480, 480, 0], 2000], [2400, 480, 0, 104000]),
('boundary control 5', [[9, 0, 0, 0, 0, 0, 0], 30], [9, 0, 0, 5]),
('normal control 6', [[481, 0, 900, 240, 540, 900, 480], 2250], [2400, 781, 360, 160931]),
('normal control 7', [[720, 540, 240, 481, 900, 780, 481], 2000], [2400, 1501, 241, 171117]),
('normal control 8', [[510, 510, 510, 480, 510, 510, 300], 2000], [2400, 930, 0, 126500])],
[('regression: seventh day detection 1', [[600, 481, 0, 540, 0, 450, 720], 2250], [2370, 421, 0, 112556]),
('regression variant: seventh day detection 2', [[780, 600, 780, 0, 600, 480, 540], 1725],
[2400, 1260, 120, 130238]),
('partial repair guard 3', [[203, 0, 480, 481, 720, 720, 540], 90], [2400, 744, 0, 5274]),
('boundary control 4', [[0, 480, 480, 480, 480, 480, 480], 1500], [2400, 480, 0, 78000]),
('boundary control 5', [[480, 480, 480, 480, 480, 480, 0], 2000], [2400, 480, 0, 104000]),
('normal control 6', [[600, 0, 600, 540, 0, 481, 450], 1725], [2370, 301, 0, 81118]),
('normal control 7', [[450, 540, 450, 240, 240, 720, 481], 1725], [2340, 780, 1, 100970]),
('normal control 8', [[481, 600, 780, 450, 0, 0, 721], 1725], [2370, 601, 61, 97563])],
[('regression: seventh day detection 1', [[481, 433, 0, 0, 450, 780, 780], 2000], [2323, 481, 120, 109483]),
('regression variant: seventh day detection 2', [[0, 0, 540, 481, 450, 540, 450], 1725],
[2340, 121, 0, 72493]),
('partial repair guard 3', [[737, 951, 39, 0, 720, 600, 240], 1500], [2199, 840, 248, 98875]),
('boundary control 4', [[27, 0, 0, 0, 0, 0, 0], 30], [27, 0, 0, 14]),
('boundary control 5', [[0, 480, 480, 480, 480, 480, 480], 1500], [2400, 480, 0, 78000]),
('normal control 6', [[780, 600, 721, 240, 780, 721, 780], 90], [2400, 1800, 422, 8916]),
('normal control 7', [[450, 0, 540, 900, 780, 0, 600], 1500], [2370, 660, 240, 96000]),
('normal control 8', [[300, 600, 300, 480, 600, 480, 600], 30], [2400, 840, 120, 1950])],
[('regression: seventh day detection 1', [[540, 176, 0, 780, 450, 0, 481], 2250], [2066, 301, 60, 98906]),
('regression variant: seventh day detection 2', [[900, 0, 720, 540, 480, 450, 780], 2250],
[2400, 1230, 240, 177188]),
('partial repair guard 3', [[450, 0, 586, 480, 900, 450, 720], 90], [2400, 1006, 180, 6404]),
('boundary control 4', [[600, 600, 600, 600, 600, 0, 0], 1500], [2400, 600, 0, 82500]),
('boundary control 5', [[27, 0, 0, 0, 0, 0, 0], 30], [27, 0, 0, 14]),
('normal control 6', [[540, 540, 780, 240, 480, 480, 0], 90], [2400, 600, 60, 5130]),
('normal control 7', [[300, 480, 600, 510, 300, 510, 300], 1725], [2400, 600, 0, 94875]),
('normal control 8', [[480, 721, 733, 899, 540, 900, 0], 90], [2400, 1500, 373, 8094])],
[('regression: seventh day detection 1', [[480, 481, 900, 721, 0, 481, 720], 1500],
[2400, 1202, 181, 114125]),
('regression variant: seventh day detection 2', [[240, 600, 0, 780, 450, 900, 900], 90],
[2400, 1050, 420, 7223]),
('partial repair guard 3', [[721, 450, 540, 0, 190, 480, 900], 90], [2400, 700, 181, 5718]),
('boundary control 4', [[540, 540, 540, 540, 540, 540, 60], 2000], [2400, 900, 0, 125000]),
('boundary control 5', [[600, 600, 600, 600, 600, 0, 0], 1500], [2400, 600, 0, 82500]),
('normal control 6', [[720, 480, 900, 600, 240, 900, 720], 2000], [2400, 1560, 600, 198000]),
('normal control 7', [[904, 780, 481, 0, 900, 0, 481], 30], [2400, 722, 424, 2166]),
('normal control 8', [[600, 720, 240, 540, 480, 450, 780], 1725], [2400, 1110, 300, 134119])]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression: seventh day detection 1 | [2370, 541, 60, 110050] | [2400, 571, 0, 108550] | Failed |
| regression variant: seventh day detection 2 | [1920, 900, 300, 5805] | [2400, 660, 60, 5265] | Failed |
| partial repair guard 3 | [2160, 1440, 540, 155250] | [2400, 1440, 300, 148350] | Failed |
| boundary control 4 | [2400, 480, 0, 104000] | [2400, 480, 0, 104000] | Passed |
| boundary control 5 | [9, 0, 0, 5] | [9, 0, 0, 5] | Passed |
| normal control 6 | [2160, 1021, 360, 165431] | [2400, 781, 360, 160931] | Failed |
| normal control 7 | [2400, 1501, 241, 171117] | [2400, 1501, 241, 171117] | Passed |
| normal control 8 | [2400, 930, 0, 126500] | [2400, 930, 0, 126500] | Passed |
SHA-256 / e9ef5465f1f646e2e9868ac6a470b0964a1d53edb2b7a25bf2ae63ccfb597306
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(days, rate):
reg = ot15 = ot2 = 0
seventh = sum(1 for m in days if m > 0) >= 6
for d, m in enumerate(days):
if d == 6 and seventh:
ot15 += min(m, 480)
ot2 += max(m - 480, 0)
continue
reg += min(m, 480)
ot15 += min(max(m - 480, 0), 240)
ot2 += max(m - 720, 0)
excess = max(reg - 2400, 0)
reg -= excess
ot15 += excess
units = reg * rate * 2 + ot15 * rate * 3 + ot2 * rate * 4
pay = (units + 60) // 120
return [reg, ot15, ot2, pay]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: seventh day detection 1', [[0, 450, 480, 480, 540, 481, 540], 2000], [2400, 571, 0, 108550]),
('regression variant: seventh day detection 2', [[240, 540, 720, 240, 600, 0, 780], 90],
[2400, 660, 60, 5265]),
('partial repair guard 3', [[720, 720, 900, 0, 780, 240, 780], 1725], [2400, 1440, 300, 148350]),
('boundary control 4', [[480, 480, 480, 480, 480, 480, 0], 2000], [2400, 480, 0, 104000]),
('boundary control 5', [[9, 0, 0, 0, 0, 0, 0], 30], [9, 0, 0, 5]),
('normal control 6', [[481, 0, 900, 240, 540, 900, 480], 2250], [2400, 781, 360, 160931]),
('normal control 7', [[720, 540, 240, 481, 900, 780, 481], 2000], [2400, 1501, 241, 171117]),
('normal control 8', [[510, 510, 510, 480, 510, 510, 300], 2000], [2400, 930, 0, 126500])],
[('regression: seventh day detection 1', [[600, 481, 0, 540, 0, 450, 720], 2250], [2370, 421, 0, 112556]),
('regression variant: seventh day detection 2', [[780, 600, 780, 0, 600, 480, 540], 1725],
[2400, 1260, 120, 130238]),
('partial repair guard 3', [[203, 0, 480, 481, 720, 720, 540], 90], [2400, 744, 0, 5274]),
('boundary control 4', [[0, 480, 480, 480, 480, 480, 480], 1500], [2400, 480, 0, 78000]),
('boundary control 5', [[480, 480, 480, 480, 480, 480, 0], 2000], [2400, 480, 0, 104000]),
('normal control 6', [[600, 0, 600, 540, 0, 481, 450], 1725], [2370, 301, 0, 81118]),
('normal control 7', [[450, 540, 450, 240, 240, 720, 481], 1725], [2340, 780, 1, 100970]),
('normal control 8', [[481, 600, 780, 450, 0, 0, 721], 1725], [2370, 601, 61, 97563])],
[('regression: seventh day detection 1', [[481, 433, 0, 0, 450, 780, 780], 2000], [2323, 481, 120, 109483]),
('regression variant: seventh day detection 2', [[0, 0, 540, 481, 450, 540, 450], 1725],
[2340, 121, 0, 72493]),
('partial repair guard 3', [[737, 951, 39, 0, 720, 600, 240], 1500], [2199, 840, 248, 98875]),
('boundary control 4', [[27, 0, 0, 0, 0, 0, 0], 30], [27, 0, 0, 14]),
('boundary control 5', [[0, 480, 480, 480, 480, 480, 480], 1500], [2400, 480, 0, 78000]),
('normal control 6', [[780, 600, 721, 240, 780, 721, 780], 90], [2400, 1800, 422, 8916]),
('normal control 7', [[450, 0, 540, 900, 780, 0, 600], 1500], [2370, 660, 240, 96000]),
('normal control 8', [[300, 600, 300, 480, 600, 480, 600], 30], [2400, 840, 120, 1950])],
[('regression: seventh day detection 1', [[540, 176, 0, 780, 450, 0, 481], 2250], [2066, 301, 60, 98906]),
('regression variant: seventh day detection 2', [[900, 0, 720, 540, 480, 450, 780], 2250],
[2400, 1230, 240, 177188]),
('partial repair guard 3', [[450, 0, 586, 480, 900, 450, 720], 90], [2400, 1006, 180, 6404]),
('boundary control 4', [[600, 600, 600, 600, 600, 0, 0], 1500], [2400, 600, 0, 82500]),
('boundary control 5', [[27, 0, 0, 0, 0, 0, 0], 30], [27, 0, 0, 14]),
('normal control 6', [[540, 540, 780, 240, 480, 480, 0], 90], [2400, 600, 60, 5130]),
('normal control 7', [[300, 480, 600, 510, 300, 510, 300], 1725], [2400, 600, 0, 94875]),
('normal control 8', [[480, 721, 733, 899, 540, 900, 0], 90], [2400, 1500, 373, 8094])],
[('regression: seventh day detection 1', [[480, 481, 900, 721, 0, 481, 720], 1500],
[2400, 1202, 181, 114125]),
('regression variant: seventh day detection 2', [[240, 600, 0, 780, 450, 900, 900], 90],
[2400, 1050, 420, 7223]),
('partial repair guard 3', [[721, 450, 540, 0, 190, 480, 900], 90], [2400, 700, 181, 5718]),
('boundary control 4', [[540, 540, 540, 540, 540, 540, 60], 2000], [2400, 900, 0, 125000]),
('boundary control 5', [[600, 600, 600, 600, 600, 0, 0], 1500], [2400, 600, 0, 82500]),
('normal control 6', [[720, 480, 900, 600, 240, 900, 720], 2000], [2400, 1560, 600, 198000]),
('normal control 7', [[904, 780, 481, 0, 900, 0, 481], 30], [2400, 722, 424, 2166]),
('normal control 8', [[600, 720, 240, 540, 480, 450, 780], 1725], [2400, 1110, 300, 134119])]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression: seventh day detection 1 | [2370, 541, 60, 110050] | [2400, 571, 0, 108550] | Failed |
| regression variant: seventh day detection 2 | [1920, 900, 300, 5805] | [2400, 660, 60, 5265] | Failed |
| partial repair guard 3 | [2160, 1440, 540, 155250] | [2400, 1440, 300, 148350] | Failed |
| boundary control 4 | [2400, 480, 0, 104000] | [2400, 480, 0, 104000] | Passed |
| boundary control 5 | [9, 0, 0, 5] | [9, 0, 0, 5] | Passed |
| normal control 6 | [2160, 1021, 360, 165431] | [2400, 781, 360, 160931] | Failed |
| normal control 7 | [2400, 1501, 241, 171117] | [2400, 1501, 241, 171117] | Passed |
| normal control 8 | [2400, 930, 0, 126500] | [2400, 930, 0, 126500] | Passed |
SHA-256 / 393207a8d7f54588f0f0a24b698ae2d43990b11117eca1009fb944cbf9898e7c
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(days, rate):
reg = ot15 = ot2 = 0
seventh = all(m > 0 for m in days)
for d, m in enumerate(days):
if d == 6 and seventh:
ot15 += min(m, 480)
ot2 += max(m - 480, 0)
continue
reg += min(m, 480)
ot15 += min(max(m - 480, 0), 240)
ot2 += max(m - 720, 0)
excess = max(reg - 2400, 0)
reg -= excess
ot15 += excess
units = reg * rate * 2 + ot15 * rate * 3 + ot2 * rate * 4
pay = (units + 60) // 120
return [reg, ot15, ot2, pay]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: seventh day detection 1', [[0, 450, 480, 480, 540, 481, 540], 2000], [2400, 571, 0, 108550]),
('regression variant: seventh day detection 2', [[240, 540, 720, 240, 600, 0, 780], 90],
[2400, 660, 60, 5265]),
('partial repair guard 3', [[720, 720, 900, 0, 780, 240, 780], 1725], [2400, 1440, 300, 148350]),
('boundary control 4', [[480, 480, 480, 480, 480, 480, 0], 2000], [2400, 480, 0, 104000]),
('boundary control 5', [[9, 0, 0, 0, 0, 0, 0], 30], [9, 0, 0, 5]),
('normal control 6', [[481, 0, 900, 240, 540, 900, 480], 2250], [2400, 781, 360, 160931]),
('normal control 7', [[720, 540, 240, 481, 900, 780, 481], 2000], [2400, 1501, 241, 171117]),
('normal control 8', [[510, 510, 510, 480, 510, 510, 300], 2000], [2400, 930, 0, 126500])],
[('regression: seventh day detection 1', [[600, 481, 0, 540, 0, 450, 720], 2250], [2370, 421, 0, 112556]),
('regression variant: seventh day detection 2', [[780, 600, 780, 0, 600, 480, 540], 1725],
[2400, 1260, 120, 130238]),
('partial repair guard 3', [[203, 0, 480, 481, 720, 720, 540], 90], [2400, 744, 0, 5274]),
('boundary control 4', [[0, 480, 480, 480, 480, 480, 480], 1500], [2400, 480, 0, 78000]),
('boundary control 5', [[480, 480, 480, 480, 480, 480, 0], 2000], [2400, 480, 0, 104000]),
('normal control 6', [[600, 0, 600, 540, 0, 481, 450], 1725], [2370, 301, 0, 81118]),
('normal control 7', [[450, 540, 450, 240, 240, 720, 481], 1725], [2340, 780, 1, 100970]),
('normal control 8', [[481, 600, 780, 450, 0, 0, 721], 1725], [2370, 601, 61, 97563])],
[('regression: seventh day detection 1', [[481, 433, 0, 0, 450, 780, 780], 2000], [2323, 481, 120, 109483]),
('regression variant: seventh day detection 2', [[0, 0, 540, 481, 450, 540, 450], 1725],
[2340, 121, 0, 72493]),
('partial repair guard 3', [[737, 951, 39, 0, 720, 600, 240], 1500], [2199, 840, 248, 98875]),
('boundary control 4', [[27, 0, 0, 0, 0, 0, 0], 30], [27, 0, 0, 14]),
('boundary control 5', [[0, 480, 480, 480, 480, 480, 480], 1500], [2400, 480, 0, 78000]),
('normal control 6', [[780, 600, 721, 240, 780, 721, 780], 90], [2400, 1800, 422, 8916]),
('normal control 7', [[450, 0, 540, 900, 780, 0, 600], 1500], [2370, 660, 240, 96000]),
('normal control 8', [[300, 600, 300, 480, 600, 480, 600], 30], [2400, 840, 120, 1950])],
[('regression: seventh day detection 1', [[540, 176, 0, 780, 450, 0, 481], 2250], [2066, 301, 60, 98906]),
('regression variant: seventh day detection 2', [[900, 0, 720, 540, 480, 450, 780], 2250],
[2400, 1230, 240, 177188]),
('partial repair guard 3', [[450, 0, 586, 480, 900, 450, 720], 90], [2400, 1006, 180, 6404]),
('boundary control 4', [[600, 600, 600, 600, 600, 0, 0], 1500], [2400, 600, 0, 82500]),
('boundary control 5', [[27, 0, 0, 0, 0, 0, 0], 30], [27, 0, 0, 14]),
('normal control 6', [[540, 540, 780, 240, 480, 480, 0], 90], [2400, 600, 60, 5130]),
('normal control 7', [[300, 480, 600, 510, 300, 510, 300], 1725], [2400, 600, 0, 94875]),
('normal control 8', [[480, 721, 733, 899, 540, 900, 0], 90], [2400, 1500, 373, 8094])],
[('regression: seventh day detection 1', [[480, 481, 900, 721, 0, 481, 720], 1500],
[2400, 1202, 181, 114125]),
('regression variant: seventh day detection 2', [[240, 600, 0, 780, 450, 900, 900], 90],
[2400, 1050, 420, 7223]),
('partial repair guard 3', [[721, 450, 540, 0, 190, 480, 900], 90], [2400, 700, 181, 5718]),
('boundary control 4', [[540, 540, 540, 540, 540, 540, 60], 2000], [2400, 900, 0, 125000]),
('boundary control 5', [[600, 600, 600, 600, 600, 0, 0], 1500], [2400, 600, 0, 82500]),
('normal control 6', [[720, 480, 900, 600, 240, 900, 720], 2000], [2400, 1560, 600, 198000]),
('normal control 7', [[904, 780, 481, 0, 900, 0, 481], 30], [2400, 722, 424, 2166]),
('normal control 8', [[600, 720, 240, 540, 480, 450, 780], 1725], [2400, 1110, 300, 134119])]]
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 fixture | Actual | Expected | Outcome |
|---|---|---|---|
| regression: seventh day detection 1 | [2400, 571, 0, 108550] | [2400, 571, 0, 108550] | Passed |
| regression variant: seventh day detection 2 | [2400, 660, 60, 5265] | [2400, 660, 60, 5265] | Passed |
| partial repair guard 3 | [2400, 1440, 300, 148350] | [2400, 1440, 300, 148350] | Passed |
| boundary control 4 | [2400, 480, 0, 104000] | [2400, 480, 0, 104000] | Passed |
| boundary control 5 | [9, 0, 0, 5] | [9, 0, 0, 5] | Passed |
| normal control 6 | [2400, 781, 360, 160931] | [2400, 781, 360, 160931] | Passed |
| normal control 7 | [2400, 1501, 241, 171117] | [2400, 1501, 241, 171117] | Passed |
| normal control 8 | [2400, 930, 0, 126500] | [2400, 930, 0, 126500] | Passed |
SHA-256 / 5bbb6bceff237f2b5bff60b34e937c42c8657672ba0e595978706225e287b3e4
Verification & scope
Stipulated toy labor rule for a bounded roster model; it is not legal advice and does not claim conformance with any jurisdiction, award, or collective agreement. 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 2026-09-29T14:51:59.090135+00:00.
Case digest / ddf1c61a09e10d15b90f17641a986bac5d3ec69b9cc0d2632ea395266af92f57