{"abstract":"Night-shift workers with overtime are underpaid on their premium.","category":"Payroll withholding rules","checks":8,"contract":"Input weekly {hours, rate, bonus, discretionary, diff_hours, diff}. Straight pay = hours*rate + diff_hours*diff. Regular rate = (straight pay + bonus unless discretionary) / hours. Overtime premium = regular rate * max(0, hours-40) / 2, rounded half-up to the cent. Total = straight pay + bonus + premium. Zero hours returns [0, bonus]. Return [premium, total].","evaluation_group":"w2-payroll-withholding-overtime-regular-rate","failed_approach":"The attempt applies the differential to every hour instead of only the differential hours.","family":"w2-payroll-withholding-overtime-regular-rate-shift-differential-in-regular-rate","id":"FA-59001","implementations":{"attempt":{"sha256":"b4775046a748631ec3fdf930d7c40b5d7e8de434c08f649bb840bafed920e408","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nimport math\nN = 1\nobservations = []\ndef solve(x):\n    h = x['hours']\n    base = h * x['rate'] + x['diff_hours'] * x['diff']\n    included = h * (x['rate'] + x['diff']) + (0 if x['discretionary'] else x['bonus'])\n    if h == 0:\n        return [0, x['bonus']]\n    ot = max(0, h - 40)\n    rr = Fraction(included, h)\n    premium = rr * ot / 2\n    prem_c = math.floor(premium + Fraction(1, 2))\n    total = base + x['bonus'] + prem_c\n    return [prem_c, total]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'hours': 56, 'rate': 2894, 'bonus': 0, 'discretionary': True, 'diff_hours': 20, 'diff': 189}, [23692, 189536]), ('regression', {'hours': 48, 'rate': 2361, 'bonus': 0, 'discretionary': False, 'diff_hours': 15, 'diff': 150}, [9632, 125210]), ('partial-repair probe', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('partial-repair probe', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('normal control', {'hours': 29, 'rate': 3185, 'bonus': 18960, 'discretionary': True, 'diff_hours': 0, 'diff': 235}, [0, 111325]), ('normal control', {'hours': 60, 'rate': 3726, 'bonus': 328, 'discretionary': False, 'diff_hours': 13, 'diff': 0}, [37315, 261203]), ('normal control', {'hours': 15, 'rate': 1913, 'bonus': 0, 'discretionary': False, 'diff_hours': 3, 'diff': 100}, [0, 28995]), ('normal control', {'hours': 36, 'rate': 1564, 'bonus': 12440, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [0, 68744])], [('regression', {'hours': 48, 'rate': 2361, 'bonus': 0, 'discretionary': False, 'diff_hours': 15, 'diff': 150}, [9632, 125210]), ('regression', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('partial-repair probe', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('partial-repair probe', {'hours': 42, 'rate': 2619, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [2619, 112617]), ('normal control', {'hours': 36, 'rate': 3124, 'bonus': 20788, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 133252]), ('normal control', {'hours': 38, 'rate': 3803, 'bonus': 0, 'discretionary': False, 'diff_hours': 5, 'diff': 175}, [0, 145389]), ('normal control', {'hours': 40, 'rate': 2368, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 94720]), ('normal control', {'hours': 15, 'rate': 1275, 'bonus': 16297, 'discretionary': False, 'diff_hours': 0, 'diff': 100}, [0, 35422])], [('regression', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('regression', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('partial-repair probe', {'hours': 42, 'rate': 2619, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [2619, 112617]), ('partial-repair probe', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('normal control', {'hours': 0, 'rate': 1235, 'bonus': 452, 'discretionary': False, 'diff_hours': 15, 'diff': 0}, [0, 452]), ('normal control', {'hours': 25, 'rate': 2784, 'bonus': 431, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [0, 70031]), ('normal control', {'hours': 49, 'rate': 2740, 'bonus': 1615, 'discretionary': True, 'diff_hours': 20, 'diff': 0}, [12330, 148205]), ('normal control', {'hours': 4, 'rate': 2630, 'bonus': 24821, 'discretionary': False, 'diff_hours': 10, 'diff': 100}, [0, 36341])], [('regression', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('regression', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('partial-repair probe', {'hours': 41, 'rate': 1051, 'bonus': 26, 'discretionary': False, 'diff_hours': 0, 'diff': 259}, [526, 43643]), ('partial-repair probe', {'hours': 41, 'rate': 1070, 'bonus': 0, 'discretionary': False, 'diff_hours': 14, 'diff': 300}, [586, 48656]), ('normal control', {'hours': 36, 'rate': 3824, 'bonus': 0, 'discretionary': False, 'diff_hours': 12, 'diff': 100}, [0, 138864]), ('normal control', {'hours': 36, 'rate': 3363, 'bonus': 0, 'discretionary': True, 'diff_hours': 0, 'diff': 0}, [0, 121068]), ('normal control', {'hours': 58, 'rate': 1280, 'bonus': 145, 'discretionary': False, 'diff_hours': 5, 'diff': 0}, [11543, 85928]), ('normal control', {'hours': 25, 'rate': 1555, 'bonus': 17670, 'discretionary': True, 'diff_hours': 0, 'diff': 150}, [0, 56545])], [('regression', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('regression', {'hours': 41, 'rate': 1070, 'bonus': 0, 'discretionary': False, 'diff_hours': 14, 'diff': 300}, [586, 48656]), ('partial-repair probe', {'hours': 52, 'rate': 2128, 'bonus': 978, 'discretionary': True, 'diff_hours': 9, 'diff': 100}, [12872, 125406]), ('partial-repair probe', {'hours': 51, 'rate': 3958, 'bonus': 443, 'discretionary': True, 'diff_hours': 0, 'diff': 100}, [21769, 224070]), ('normal control', {'hours': 13, 'rate': 3420, 'bonus': 0, 'discretionary': True, 'diff_hours': 0, 'diff': 150}, [0, 44460]), ('normal control', {'hours': 17, 'rate': 3366, 'bonus': 396, 'discretionary': True, 'diff_hours': 8, 'diff': 0}, [0, 57618]), ('normal control', {'hours': 1, 'rate': 1458, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 1458]), ('normal control', {'hours': 40, 'rate': 2719, 'bonus': 0, 'discretionary': False, 'diff_hours': 13, 'diff': 100}, [0, 110060])]]\nfor i, (label, args, expected) in enumerate(fixtures[N-1]):\n    check(\"%s %d\" % (label, i), solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"broken":{"sha256":"4ce12715a4f961d3d7645d4a95c84bf69721d143cad0efa36e6d556de6305183","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nimport math\nN = 1\nobservations = []\ndef solve(x):\n    h = x['hours']\n    base = h * x['rate'] + x['diff_hours'] * x['diff']\n    included = h * x['rate'] + (0 if x['discretionary'] else x['bonus'])\n    if h == 0:\n        return [0, x['bonus']]\n    ot = max(0, h - 40)\n    rr = Fraction(included, h)\n    premium = rr * ot / 2\n    prem_c = math.floor(premium + Fraction(1, 2))\n    total = base + x['bonus'] + prem_c\n    return [prem_c, total]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'hours': 56, 'rate': 2894, 'bonus': 0, 'discretionary': True, 'diff_hours': 20, 'diff': 189}, [23692, 189536]), ('regression', {'hours': 48, 'rate': 2361, 'bonus': 0, 'discretionary': False, 'diff_hours': 15, 'diff': 150}, [9632, 125210]), ('partial-repair probe', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('partial-repair probe', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('normal control', {'hours': 29, 'rate': 3185, 'bonus': 18960, 'discretionary': True, 'diff_hours': 0, 'diff': 235}, [0, 111325]), ('normal control', {'hours': 60, 'rate': 3726, 'bonus': 328, 'discretionary': False, 'diff_hours': 13, 'diff': 0}, [37315, 261203]), ('normal control', {'hours': 15, 'rate': 1913, 'bonus': 0, 'discretionary': False, 'diff_hours': 3, 'diff': 100}, [0, 28995]), ('normal control', {'hours': 36, 'rate': 1564, 'bonus': 12440, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [0, 68744])], [('regression', {'hours': 48, 'rate': 2361, 'bonus': 0, 'discretionary': False, 'diff_hours': 15, 'diff': 150}, [9632, 125210]), ('regression', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('partial-repair probe', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('partial-repair probe', {'hours': 42, 'rate': 2619, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [2619, 112617]), ('normal control', {'hours': 36, 'rate': 3124, 'bonus': 20788, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 133252]), ('normal control', {'hours': 38, 'rate': 3803, 'bonus': 0, 'discretionary': False, 'diff_hours': 5, 'diff': 175}, [0, 145389]), ('normal control', {'hours': 40, 'rate': 2368, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 94720]), ('normal control', {'hours': 15, 'rate': 1275, 'bonus': 16297, 'discretionary': False, 'diff_hours': 0, 'diff': 100}, [0, 35422])], [('regression', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('regression', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('partial-repair probe', {'hours': 42, 'rate': 2619, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [2619, 112617]), ('partial-repair probe', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('normal control', {'hours': 0, 'rate': 1235, 'bonus': 452, 'discretionary': False, 'diff_hours': 15, 'diff': 0}, [0, 452]), ('normal control', {'hours': 25, 'rate': 2784, 'bonus': 431, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [0, 70031]), ('normal control', {'hours': 49, 'rate': 2740, 'bonus': 1615, 'discretionary': True, 'diff_hours': 20, 'diff': 0}, [12330, 148205]), ('normal control', {'hours': 4, 'rate': 2630, 'bonus': 24821, 'discretionary': False, 'diff_hours': 10, 'diff': 100}, [0, 36341])], [('regression', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('regression', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('partial-repair probe', {'hours': 41, 'rate': 1051, 'bonus': 26, 'discretionary': False, 'diff_hours': 0, 'diff': 259}, [526, 43643]), ('partial-repair probe', {'hours': 41, 'rate': 1070, 'bonus': 0, 'discretionary': False, 'diff_hours': 14, 'diff': 300}, [586, 48656]), ('normal control', {'hours': 36, 'rate': 3824, 'bonus': 0, 'discretionary': False, 'diff_hours': 12, 'diff': 100}, [0, 138864]), ('normal control', {'hours': 36, 'rate': 3363, 'bonus': 0, 'discretionary': True, 'diff_hours': 0, 'diff': 0}, [0, 121068]), ('normal control', {'hours': 58, 'rate': 1280, 'bonus': 145, 'discretionary': False, 'diff_hours': 5, 'diff': 0}, [11543, 85928]), ('normal control', {'hours': 25, 'rate': 1555, 'bonus': 17670, 'discretionary': True, 'diff_hours': 0, 'diff': 150}, [0, 56545])], [('regression', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('regression', {'hours': 41, 'rate': 1070, 'bonus': 0, 'discretionary': False, 'diff_hours': 14, 'diff': 300}, [586, 48656]), ('partial-repair probe', {'hours': 52, 'rate': 2128, 'bonus': 978, 'discretionary': True, 'diff_hours': 9, 'diff': 100}, [12872, 125406]), ('partial-repair probe', {'hours': 51, 'rate': 3958, 'bonus': 443, 'discretionary': True, 'diff_hours': 0, 'diff': 100}, [21769, 224070]), ('normal control', {'hours': 13, 'rate': 3420, 'bonus': 0, 'discretionary': True, 'diff_hours': 0, 'diff': 150}, [0, 44460]), ('normal control', {'hours': 17, 'rate': 3366, 'bonus': 396, 'discretionary': True, 'diff_hours': 8, 'diff': 0}, [0, 57618]), ('normal control', {'hours': 1, 'rate': 1458, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 1458]), ('normal control', {'hours': 40, 'rate': 2719, 'bonus': 0, 'discretionary': False, 'diff_hours': 13, 'diff': 100}, [0, 110060])]]\nfor i, (label, args, expected) in enumerate(fixtures[N-1]):\n    check(\"%s %d\" % (label, i), solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"},"fixed":{"sha256":"64cfecd3fcc8f8f5eecf3778e545cfa2fbd109b20262d6718d9e723ad594f944","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nimport math\nN = 1\nobservations = []\ndef solve(x):\n    h = x['hours']\n    base = h * x['rate'] + x['diff_hours'] * x['diff']\n    included = base + (0 if x['discretionary'] else x['bonus'])\n    if h == 0:\n        return [0, x['bonus']]\n    ot = max(0, h - 40)\n    rr = Fraction(included, h)\n    premium = rr * ot / 2\n    prem_c = math.floor(premium + Fraction(1, 2))\n    total = base + x['bonus'] + prem_c\n    return [prem_c, total]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'hours': 56, 'rate': 2894, 'bonus': 0, 'discretionary': True, 'diff_hours': 20, 'diff': 189}, [23692, 189536]), ('regression', {'hours': 48, 'rate': 2361, 'bonus': 0, 'discretionary': False, 'diff_hours': 15, 'diff': 150}, [9632, 125210]), ('partial-repair probe', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('partial-repair probe', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('normal control', {'hours': 29, 'rate': 3185, 'bonus': 18960, 'discretionary': True, 'diff_hours': 0, 'diff': 235}, [0, 111325]), ('normal control', {'hours': 60, 'rate': 3726, 'bonus': 328, 'discretionary': False, 'diff_hours': 13, 'diff': 0}, [37315, 261203]), ('normal control', {'hours': 15, 'rate': 1913, 'bonus': 0, 'discretionary': False, 'diff_hours': 3, 'diff': 100}, [0, 28995]), ('normal control', {'hours': 36, 'rate': 1564, 'bonus': 12440, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [0, 68744])], [('regression', {'hours': 48, 'rate': 2361, 'bonus': 0, 'discretionary': False, 'diff_hours': 15, 'diff': 150}, [9632, 125210]), ('regression', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('partial-repair probe', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('partial-repair probe', {'hours': 42, 'rate': 2619, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [2619, 112617]), ('normal control', {'hours': 36, 'rate': 3124, 'bonus': 20788, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 133252]), ('normal control', {'hours': 38, 'rate': 3803, 'bonus': 0, 'discretionary': False, 'diff_hours': 5, 'diff': 175}, [0, 145389]), ('normal control', {'hours': 40, 'rate': 2368, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 94720]), ('normal control', {'hours': 15, 'rate': 1275, 'bonus': 16297, 'discretionary': False, 'diff_hours': 0, 'diff': 100}, [0, 35422])], [('regression', {'hours': 45, 'rate': 1070, 'bonus': 6593, 'discretionary': True, 'diff_hours': 3, 'diff': 100}, [2692, 57735]), ('regression', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('partial-repair probe', {'hours': 42, 'rate': 2619, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [2619, 112617]), ('partial-repair probe', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('normal control', {'hours': 0, 'rate': 1235, 'bonus': 452, 'discretionary': False, 'diff_hours': 15, 'diff': 0}, [0, 452]), ('normal control', {'hours': 25, 'rate': 2784, 'bonus': 431, 'discretionary': False, 'diff_hours': 0, 'diff': 150}, [0, 70031]), ('normal control', {'hours': 49, 'rate': 2740, 'bonus': 1615, 'discretionary': True, 'diff_hours': 20, 'diff': 0}, [12330, 148205]), ('normal control', {'hours': 4, 'rate': 2630, 'bonus': 24821, 'discretionary': False, 'diff_hours': 10, 'diff': 100}, [0, 36341])], [('regression', {'hours': 51, 'rate': 1430, 'bonus': 583, 'discretionary': True, 'diff_hours': 17, 'diff': 212}, [8254, 85371]), ('regression', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('partial-repair probe', {'hours': 41, 'rate': 1051, 'bonus': 26, 'discretionary': False, 'diff_hours': 0, 'diff': 259}, [526, 43643]), ('partial-repair probe', {'hours': 41, 'rate': 1070, 'bonus': 0, 'discretionary': False, 'diff_hours': 14, 'diff': 300}, [586, 48656]), ('normal control', {'hours': 36, 'rate': 3824, 'bonus': 0, 'discretionary': False, 'diff_hours': 12, 'diff': 100}, [0, 138864]), ('normal control', {'hours': 36, 'rate': 3363, 'bonus': 0, 'discretionary': True, 'diff_hours': 0, 'diff': 0}, [0, 121068]), ('normal control', {'hours': 58, 'rate': 1280, 'bonus': 145, 'discretionary': False, 'diff_hours': 5, 'diff': 0}, [11543, 85928]), ('normal control', {'hours': 25, 'rate': 1555, 'bonus': 17670, 'discretionary': True, 'diff_hours': 0, 'diff': 150}, [0, 56545])], [('regression', {'hours': 42, 'rate': 1092, 'bonus': 0, 'discretionary': False, 'diff_hours': 2, 'diff': 157}, [1099, 47277]), ('regression', {'hours': 41, 'rate': 1070, 'bonus': 0, 'discretionary': False, 'diff_hours': 14, 'diff': 300}, [586, 48656]), ('partial-repair probe', {'hours': 52, 'rate': 2128, 'bonus': 978, 'discretionary': True, 'diff_hours': 9, 'diff': 100}, [12872, 125406]), ('partial-repair probe', {'hours': 51, 'rate': 3958, 'bonus': 443, 'discretionary': True, 'diff_hours': 0, 'diff': 100}, [21769, 224070]), ('normal control', {'hours': 13, 'rate': 3420, 'bonus': 0, 'discretionary': True, 'diff_hours': 0, 'diff': 150}, [0, 44460]), ('normal control', {'hours': 17, 'rate': 3366, 'bonus': 396, 'discretionary': True, 'diff_hours': 8, 'diff': 0}, [0, 57618]), ('normal control', {'hours': 1, 'rate': 1458, 'bonus': 0, 'discretionary': False, 'diff_hours': 0, 'diff': 0}, [0, 1458]), ('normal control', {'hours': 40, 'rate': 2719, 'bonus': 0, 'discretionary': False, 'diff_hours': 13, 'diff': 100}, [0, 110060])]]\nfor i, (label, args, expected) in enumerate(fixtures[N-1]):\n    check(\"%s %d\" % (label, i), solve(args), expected)\nprint(json.dumps({\"observations\": observations, \"passed\": all(x[\"passed\"] for x in observations)}, ensure_ascii=False))\nraise SystemExit(0 if all(x[\"passed\"] for x in observations) else 1)\n"}},"limitations":"A deterministic teaching model of a stipulated payroll rule with toy thresholds and rates. It makes no claim of conformance to any tax authority, statute or jurisdiction and is not payroll software. 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.","method":"Deterministic executable model with adversarial boundary fixtures.","provenance":{"created_by":"Failure Map","dependencies":"Python standard library","family":"w2-payroll-withholding-overtime-regular-rate-shift-differential-in-regular-rate","generated_at":"2026-09-29T14:46:32.085476+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Nondiscretionary bonuses and shift differentials raise the regular rate, and only a half-time premium is owed on top of straight-time pay.","repair":"Restore the contract rule at the shift differential in regular rate step: use `included = base +`.","root_cause":"Shift-differential pay is left out of the compensation used for the regular rate.","sha256":"dbf59ba4d6a3394b1727ac950ce5128a237da0a8f5ef6d2d6e941109da5c7c1c","title":"Overtime regular rate with bonuses and differentials: shift differential in regular rate · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":43.274,"exit_code":1,"observations":[{"actual":[24664,190508],"check":"regression 0","expected":[23692,189536],"passed":false},{"actual":[10044,125622],"check":"regression 1","expected":[9632,125210],"passed":false},{"actual":[2925,57968],"check":"partial-repair probe 2","expected":[2692,57735],"passed":false},{"actual":[9031,86148],"check":"partial-repair probe 3","expected":[8254,85371],"passed":false},{"actual":[0,111325],"check":"normal control 4","expected":[0,111325],"passed":true},{"actual":[37315,261203],"check":"normal control 5","expected":[37315,261203],"passed":true},{"actual":[0,28995],"check":"normal control 6","expected":[0,28995],"passed":true},{"actual":[0,68744],"check":"normal control 7","expected":[0,68744],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 0\", \"actual\": [24664, 190508], \"expected\": [23692, 189536], \"passed\": false}, {\"check\": \"regression 1\", \"actual\": [10044, 125622], \"expected\": [9632, 125210], \"passed\": false}, {\"check\": \"partial-repair probe 2\", \"actual\": [2925, 57968], \"expected\": [2692, 57735], \"passed\": false}, {\"check\": \"partial-repair probe 3\", \"actual\": [9031, 86148], \"expected\": [8254, 85371], \"passed\": false}, {\"check\": \"normal control 4\", \"actual\": [0, 111325], \"expected\": [0, 111325], \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": [37315, 261203], \"expected\": [37315, 261203], \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": [0, 28995], \"expected\": [0, 28995], \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": [0, 68744], \"expected\": [0, 68744], \"passed\": true}], \"passed\": 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