{"abstract":"Employees with a very low cash rate have more tip credit claimed than the jurisdiction maximum.","category":"Payroll withholding rules","checks":8,"contract":"Input weekly {hours, cash_rate, tips, min_wage (even cents), tip_credit_max}. Tip credit per hour = max(0, min(min_wage - cash_rate, tip_credit_max)). Hours over 40 are paid at 1.5*min_wage minus the same credit. Credit actually taken = min(tips, hours*credit); the unmet credit is paid as makeup. Return [cash_wages, makeup].","contract_signature":"x","evaluation_group":"w2-payroll-withholding-tipped-employee-makeup","failed_approach":"The attempt always claims the maximum credit, even when the cash rate leaves a smaller gap.","family":"w2-payroll-withholding-tipped-employee-makeup-tip-credit-cap","id":"FA-58976","implementations":{"attempt":{"sha256":"17af783dd31200df0417e61b8026d2704780a7bb643c6693f445acde4e449ec0","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    h = x['hours']\n    mw = x['min_wage']\n    cash = x['cash_rate']\n    reg = min(h, 40)\n    ot = max(0, h - 40)\n    credit_rate = max(0, x['tip_credit_max'])\n    credit_total = min(x['tips'], h * credit_rate)\n    makeup = h * credit_rate - credit_total\n    cash_wages = reg * (mw - credit_rate) + ot * (mw * 3 // 2 - credit_rate) + makeup\n    return [cash_wages, makeup]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'hours': 42, 'cash_rate': 300, 'tips': 0, 'min_wage': 1600, 'tip_credit_max': 800}, [68800, 33600]), ('regression', {'hours': 21, 'cash_rate': 500, 'tips': 46641, 'min_wage': 1200, 'tip_credit_max': 512}, [14448, 0]), ('partial-repair probe', {'hours': 47, 'cash_rate': 1200, 'tips': 1983, 'min_wage': 1200, 'tip_credit_max': 300}, [60600, 0]), ('partial-repair probe', {'hours': 23, 'cash_rate': 776, 'tips': 1403, 'min_wage': 726, 'tip_credit_max': 800}, [16698, 0]), ('normal control', {'hours': 0, 'cash_rate': 500, 'tips': 17501, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 213, 'tips': 48885, 'min_wage': 1000, 'tip_credit_max': 800}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 1600, 'tips': 15944, 'min_wage': 1600, 'tip_credit_max': 800}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2628, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0])], [('regression', {'hours': 21, 'cash_rate': 500, 'tips': 46641, 'min_wage': 1200, 'tip_credit_max': 512}, [14448, 0]), ('regression', {'hours': 58, 'cash_rate': 300, 'tips': 43606, 'min_wage': 1200, 'tip_credit_max': 300}, [63000, 0]), ('partial-repair probe', {'hours': 27, 'cash_rate': 726, 'tips': 397, 'min_wage': 726, 'tip_credit_max': 300}, [19602, 0]), ('partial-repair probe', {'hours': 40, 'cash_rate': 1600, 'tips': 1562, 'min_wage': 1600, 'tip_credit_max': 300}, [64000, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2732, 'min_wage': 1600, 'tip_credit_max': 512}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 500, 'tips': 17501, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 213, 'tips': 48885, 'min_wage': 1000, 'tip_credit_max': 800}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 1600, 'tips': 15944, 'min_wage': 1600, 'tip_credit_max': 800}, [0, 0])], [('regression', {'hours': 58, 'cash_rate': 300, 'tips': 43606, 'min_wage': 1200, 'tip_credit_max': 300}, [63000, 0]), ('regression', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('partial-repair probe', {'hours': 19, 'cash_rate': 300, 'tips': 3831, 'min_wage': 726, 'tip_credit_max': 512}, [9963, 4263]), ('partial-repair probe', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2628, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2732, 'min_wage': 1600, 'tip_credit_max': 512}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 500, 'tips': 17501, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 213, 'tips': 48885, 'min_wage': 1000, 'tip_credit_max': 800}, [0, 0])], [('regression', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('regression', {'hours': 40, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 300}, [40000, 12000]), ('partial-repair probe', {'hours': 22, 'cash_rate': 1200, 'tips': 48563, 'min_wage': 1200, 'tip_credit_max': 300}, [26400, 0]), ('partial-repair probe', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('normal control', {'hours': 0, 'cash_rate': 1600, 'tips': 15944, 'min_wage': 1600, 'tip_credit_max': 800}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2628, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2732, 'min_wage': 1600, 'tip_credit_max': 512}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 500, 'tips': 17501, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0])], [('regression', {'hours': 40, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 300}, [40000, 12000]), ('regression', {'hours': 13, 'cash_rate': 300, 'tips': 3600, 'min_wage': 1600, 'tip_credit_max': 512}, [17200, 3056]), ('partial-repair probe', {'hours': 15, 'cash_rate': 300, 'tips': 777, 'min_wage': 726, 'tip_credit_max': 800}, [10113, 5613]), ('partial-repair probe', {'hours': 47, 'cash_rate': 1650, 'tips': 42376, 'min_wage': 1600, 'tip_credit_max': 300}, [80800, 0]), ('normal control', {'hours': 0, 'cash_rate': 213, 'tips': 48885, 'min_wage': 1000, 'tip_credit_max': 800}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 1600, 'tips': 15944, 'min_wage': 1600, 'tip_credit_max': 800}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2628, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2732, 'min_wage': 1600, 'tip_credit_max': 512}, [0, 0])]]\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":"428a6c1220cee288d47da32d9037fa38dabc22008b4b807660ade6cf927485b0","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\n\nN = 1\nobservations = []\ndef solve(x):\n    h = x['hours']\n    mw = x['min_wage']\n    cash = x['cash_rate']\n    reg = min(h, 40)\n    ot = max(0, h - 40)\n    credit_rate = max(0, mw - cash)\n    credit_total = min(x['tips'], h * credit_rate)\n    makeup = h * credit_rate - credit_total\n    cash_wages = reg * (mw - credit_rate) + ot * (mw * 3 // 2 - credit_rate) + makeup\n    return [cash_wages, makeup]\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[('regression', {'hours': 42, 'cash_rate': 300, 'tips': 0, 'min_wage': 1600, 'tip_credit_max': 800}, [68800, 33600]), ('regression', {'hours': 21, 'cash_rate': 500, 'tips': 46641, 'min_wage': 1200, 'tip_credit_max': 512}, [14448, 0]), ('partial-repair probe', {'hours': 47, 'cash_rate': 1200, 'tips': 1983, 'min_wage': 1200, 'tip_credit_max': 300}, [60600, 0]), ('partial-repair probe', {'hours': 23, 'cash_rate': 776, 'tips': 1403, 'min_wage': 726, 'tip_credit_max': 800}, [16698, 0]), ('normal control', {'hours': 0, 'cash_rate': 500, 'tips': 17501, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 213, 'tips': 48885, 'min_wage': 1000, 'tip_credit_max': 800}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 1600, 'tips': 15944, 'min_wage': 1600, 'tip_credit_max': 800}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2628, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0])], [('regression', {'hours': 21, 'cash_rate': 500, 'tips': 46641, 'min_wage': 1200, 'tip_credit_max': 512}, [14448, 0]), ('regression', {'hours': 58, 'cash_rate': 300, 'tips': 43606, 'min_wage': 1200, 'tip_credit_max': 300}, [63000, 0]), ('partial-repair probe', {'hours': 27, 'cash_rate': 726, 'tips': 397, 'min_wage': 726, 'tip_credit_max': 300}, [19602, 0]), ('partial-repair probe', {'hours': 40, 'cash_rate': 1600, 'tips': 1562, 'min_wage': 1600, 'tip_credit_max': 300}, [64000, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2732, 'min_wage': 1600, 'tip_credit_max': 512}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 500, 'tips': 17501, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 213, 'tips': 48885, 'min_wage': 1000, 'tip_credit_max': 800}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 1600, 'tips': 15944, 'min_wage': 1600, 'tip_credit_max': 800}, [0, 0])], [('regression', {'hours': 58, 'cash_rate': 300, 'tips': 43606, 'min_wage': 1200, 'tip_credit_max': 300}, [63000, 0]), ('regression', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('partial-repair probe', {'hours': 19, 'cash_rate': 300, 'tips': 3831, 'min_wage': 726, 'tip_credit_max': 512}, [9963, 4263]), ('partial-repair probe', {'hours': 56, 'cash_rate': 213, 'tips': 464, 'min_wage': 1000, 'tip_credit_max': 800}, [63536, 43608]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2628, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2732, 'min_wage': 1600, 'tip_credit_max': 512}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 500, 'tips': 17501, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 213, 'tips': 48885, 'min_wage': 1000, 'tip_credit_max': 800}, [0, 0])], [('regression', {'hours': 52, 'cash_rate': 213, 'tips': 42918, 'min_wage': 1200, 'tip_credit_max': 300}, [54000, 0]), ('regression', {'hours': 40, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 300}, [40000, 12000]), ('partial-repair probe', {'hours': 22, 'cash_rate': 1200, 'tips': 48563, 'min_wage': 1200, 'tip_credit_max': 300}, [26400, 0]), ('partial-repair probe', {'hours': 52, 'cash_rate': 300, 'tips': 13052, 'min_wage': 1000, 'tip_credit_max': 800}, [44948, 23348]), ('normal control', {'hours': 0, 'cash_rate': 1600, 'tips': 15944, 'min_wage': 1600, 'tip_credit_max': 800}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2628, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2732, 'min_wage': 1600, 'tip_credit_max': 512}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 500, 'tips': 17501, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0])], [('regression', {'hours': 40, 'cash_rate': 213, 'tips': 0, 'min_wage': 1000, 'tip_credit_max': 300}, [40000, 12000]), ('regression', {'hours': 13, 'cash_rate': 300, 'tips': 3600, 'min_wage': 1600, 'tip_credit_max': 512}, [17200, 3056]), ('partial-repair probe', {'hours': 15, 'cash_rate': 300, 'tips': 777, 'min_wage': 726, 'tip_credit_max': 800}, [10113, 5613]), ('partial-repair probe', {'hours': 47, 'cash_rate': 1650, 'tips': 42376, 'min_wage': 1600, 'tip_credit_max': 300}, [80800, 0]), ('normal control', {'hours': 0, 'cash_rate': 213, 'tips': 48885, 'min_wage': 1000, 'tip_credit_max': 800}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 1600, 'tips': 15944, 'min_wage': 1600, 'tip_credit_max': 800}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2628, 'min_wage': 1600, 'tip_credit_max': 300}, [0, 0]), ('normal control', {'hours': 0, 'cash_rate': 300, 'tips': 2732, 'min_wage': 1600, 'tip_credit_max': 512}, [0, 0])]]\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-tipped-employee-makeup-tip-credit-cap","generated_at":"2026-09-29T14:46:31.837477+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Tip credits are capped by tips actually received and do not grow on overtime hours.","root_cause":"The credit per hour is the full gap to minimum wage without applying the maximum credit cap.","sha256":"dd9ce6619edbbc3e32fe4c624638ea5b36314d21ee217357b31585aac6042edd","title":"Tip credit and minimum-wage makeup: tip credit cap · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verified":true,"visibility":"public","verification":{"attempt":{"elapsed_ms":41.586,"exit_code":1,"observations":[{"actual":[68800,33600],"check":"regression 0","expected":[68800,33600],"passed":true},{"actual":[14448,0],"check":"regression 1","expected":[14448,0],"passed":true},{"actual":[58617,12117],"check":"partial-repair probe 2","expected":[60600,0],"passed":false},{"actual":[15295,16997],"check":"partial-repair probe 3","expected":[16698,0],"passed":false},{"actual":[0,0],"check":"normal control 4","expected":[0,0],"passed":true},{"actual":[0,0],"check":"normal control 5","expected":[0,0],"passed":true},{"actual":[0,0],"check":"normal control 6","expected":[0,0],"passed":true},{"actual":[0,0],"check":"normal control 7","expected":[0,0],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 0\", \"actual\": [68800, 33600], \"expected\": [68800, 33600], \"passed\": true}, {\"check\": \"regression 1\", \"actual\": [14448, 0], \"expected\": [14448, 0], \"passed\": true}, {\"check\": \"partial-repair probe 2\", \"actual\": [58617, 12117], \"expected\": [60600, 0], \"passed\": false}, {\"check\": \"partial-repair probe 3\", \"actual\": [15295, 16997], \"expected\": [16698, 0], \"passed\": false}, {\"check\": \"normal control 4\", \"actual\": [0, 0], \"expected\": [0, 0], \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": [0, 0], \"expected\": [0, 0], \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": [0, 0], \"expected\": [0, 0], \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": [0, 0], \"expected\": [0, 0], \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":40.274,"exit_code":1,"observations":[{"actual":[68800,54600],"check":"regression 0","expected":[68800,33600],"passed":false},{"actual":[10500,0],"check":"regression 1","expected":[14448,0],"passed":false},{"actual":[60600,0],"check":"partial-repair probe 2","expected":[60600,0],"passed":true},{"actual":[16698,0],"check":"partial-repair probe 3","expected":[16698,0],"passed":true},{"actual":[0,0],"check":"normal control 4","expected":[0,0],"passed":true},{"actual":[0,0],"check":"normal control 5","expected":[0,0],"passed":true},{"actual":[0,0],"check":"normal control 6","expected":[0,0],"passed":true},{"actual":[0,0],"check":"normal control 7","expected":[0,0],"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"regression 0\", \"actual\": [68800, 54600], \"expected\": [68800, 33600], \"passed\": false}, {\"check\": \"regression 1\", \"actual\": [10500, 0], \"expected\": [14448, 0], \"passed\": false}, {\"check\": \"partial-repair probe 2\", \"actual\": [60600, 0], \"expected\": [60600, 0], \"passed\": true}, {\"check\": \"partial-repair probe 3\", \"actual\": [16698, 0], \"expected\": [16698, 0], \"passed\": true}, {\"check\": \"normal control 4\", \"actual\": [0, 0], \"expected\": [0, 0], \"passed\": true}, {\"check\": \"normal control 5\", \"actual\": [0, 0], \"expected\": [0, 0], \"passed\": true}, {\"check\": \"normal control 6\", \"actual\": [0, 0], \"expected\": [0, 0], \"passed\": true}, {\"check\": \"normal control 7\", \"actual\": [0, 0], \"expected\": [0, 0], \"passed\": true}], \"passed\": false}\n"}},"member_only":{"stages":["fixed"],"fields":["implementations.fixed","verification.fixed","harness","repair"],"note":"The verified repair, its recorded checks, the repair description, and the scoring harness are available to members."}}