{"abstract":"Demand-responsive meter rate adjustment returns a wrong result when the upper band uses the peak hour.","category":"Toll and parking fee computation","checks":8,"contract":"Input {rate, occupancy: hourly percentages, hi, lo, step, min_rate, max_rate, min_samples}. With fewer than min_samples samples the rate is unchanged. Otherwise the exact mean occupancy above hi raises the rate by step, below lo lowers it by step; the result is always clamped to [min_rate, max_rate].","contract_signature":"x","evaluation_group":"w2-toll_and_parking_fee_computation-demand-meter-rate","failed_approach":"A single busy hour raises the rate even when the mean is in band.","family":"w2-toll_and_parking_fee_computation-demand-meter-rate-hi-boundary","id":"FA-68761","implementations":{"attempt":{"sha256":"9dfc73d6c051f3ae76374852455b7887a5eb903e502eb177bcc9d389eb763ace","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(x):\n    occ = x['occupancy']\n    if len(occ) < x['min_samples']:\n        return x['rate']\n    avg = Fraction(sum(occ), len(occ))\n    r = x['rate']\n    if max(occ) > x['hi']:\n        r += x['step']\n    elif avg < x['lo']:\n        r -= x['step']\n    return max(x['min_rate'], min(x['max_rate'], r))\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 175, 'occupancy': [81, 93, 83, 80, 60, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 175), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 275, 'occupancy': [80, 80, 59, 60, 89, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 275), ({'rate': 200, 'occupancy': [59, 84], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 200), ({'rate': 425, 'occupancy': [], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 425)], [({'rate': 200, 'occupancy': [80, 59, 81, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 200), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 325, 'occupancy': [81, 60, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 325), ({'rate': 175, 'occupancy': [], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 175), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [80, 80, 80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 300), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 150, 'occupancy': [60, 99, 80, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 150)], [({'rate': 175, 'occupancy': [80, 60, 95], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 175), ({'rate': 300, 'occupancy': [60, 81, 60, 60, 59, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 300), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 575, 'occupancy': [81, 81, 87, 71], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 575), ({'rate': 525, 'occupancy': [60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 525)], [({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 725, 'occupancy': [60, 80, 87], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 600), ({'rate': 125, 'occupancy': [81, 81, 62], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 125), ({'rate': 150, 'occupancy': [59, 100, 80, 81, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 150), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 725, 'occupancy': [95, 80, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 250, 'occupancy': [80, 59, 80, 59, 81, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 250)], [({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 325, 'occupancy': [46, 80, 59, 81, 79, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 100, 'occupancy': [80, 97, 81, 51, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 175, 'occupancy': [80, 80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 175), ({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 725, 'occupancy': [59, 81, 59, 55], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300)]]\nfor i, (args, expected) in enumerate(fixtures[N-1]):\n    check('fee oracle' + ' %d' % 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":"820b518bb386e1c56c4da40d14b93234f58a3365d7ad6c61343bdda4a4a3ff87","source":"\"\"\"Failure Map reference implementation. Python standard library only.\"\"\"\nimport json\nfrom fractions import Fraction\nN = 1\nobservations = []\ndef solve(x):\n    occ = x['occupancy']\n    if len(occ) < x['min_samples']:\n        return x['rate']\n    avg = Fraction(sum(occ), len(occ))\n    r = x['rate']\n    if avg >= x['hi']:\n        r += x['step']\n    elif avg < x['lo']:\n        r -= x['step']\n    return max(x['min_rate'], min(x['max_rate'], r))\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 175, 'occupancy': [81, 93, 83, 80, 60, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 175), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 275, 'occupancy': [80, 80, 59, 60, 89, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 275), ({'rate': 200, 'occupancy': [59, 84], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 200), ({'rate': 425, 'occupancy': [], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 425)], [({'rate': 200, 'occupancy': [80, 59, 81, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 200), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 325, 'occupancy': [81, 60, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 325), ({'rate': 175, 'occupancy': [], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 175), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [80, 80, 80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 300), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 150, 'occupancy': [60, 99, 80, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 150)], [({'rate': 175, 'occupancy': [80, 60, 95], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 175), ({'rate': 300, 'occupancy': [60, 81, 60, 60, 59, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 300), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 575, 'occupancy': [81, 81, 87, 71], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 575), ({'rate': 525, 'occupancy': [60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 525)], [({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 725, 'occupancy': [60, 80, 87], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 600), ({'rate': 125, 'occupancy': [81, 81, 62], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 125), ({'rate': 150, 'occupancy': [59, 100, 80, 81, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 150), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 725, 'occupancy': [95, 80, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 250, 'occupancy': [80, 59, 80, 59, 81, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 250)], [({'rate': 300, 'occupancy': [90, 90], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 325, 'occupancy': [46, 80, 59, 81, 79, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 100, 'occupancy': [80, 97, 81, 51, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100), ({'rate': 800, 'occupancy': [70, 70, 70], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 175, 'occupancy': [80, 80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 3}, 175), ({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 725, 'occupancy': [59, 81, 59, 55], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300)]]\nfor i, (args, expected) in enumerate(fixtures[N-1]):\n    check('fee oracle' + ' %d' % 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, bounded toy model with a stipulated contract; it makes no claim of conformance to any agency manual or standard. 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-toll_and_parking_fee_computation-demand-meter-rate-hi-boundary","generated_at":"2026-09-29T14:48:05.196375+00:00","license":"CC0-1.0","python":"3.12.14","seed":1,"split":"open-access"},"relevance":"Fee engines bill customers in integer cents; a wrong boundary, rounding stage or cap scope silently over- or under-charges.","root_cause":"A mean of exactly the target maximum raises the rate.","sha256":"e3b99d3345b79146c23f3272a5bf7e166315116f41528a00f142764a72627081","title":"Demand-responsive meter rate adjustment: the upper band uses the peak hour · 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":47.041,"exit_code":1,"observations":[{"actual":275,"check":"fee oracle 0","expected":275,"passed":true},{"actual":225,"check":"fee oracle 1","expected":175,"passed":false},{"actual":300,"check":"fee oracle 2","expected":300,"passed":true},{"actual":325,"check":"fee oracle 3","expected":325,"passed":true},{"actual":300,"check":"fee oracle 4","expected":300,"passed":true},{"actual":325,"check":"fee oracle 5","expected":275,"passed":false},{"actual":200,"check":"fee oracle 6","expected":200,"passed":true},{"actual":425,"check":"fee oracle 7","expected":425,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"fee oracle 0\", \"actual\": 275, \"expected\": 275, \"passed\": true}, {\"check\": \"fee oracle 1\", \"actual\": 225, \"expected\": 175, \"passed\": false}, {\"check\": \"fee oracle 2\", \"actual\": 300, \"expected\": 300, \"passed\": true}, {\"check\": \"fee oracle 3\", \"actual\": 325, \"expected\": 325, \"passed\": true}, {\"check\": \"fee oracle 4\", \"actual\": 300, \"expected\": 300, \"passed\": true}, {\"check\": \"fee oracle 5\", \"actual\": 325, \"expected\": 275, \"passed\": false}, {\"check\": \"fee oracle 6\", \"actual\": 200, \"expected\": 200, \"passed\": true}, {\"check\": \"fee oracle 7\", \"actual\": 425, \"expected\": 425, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":43.778,"exit_code":1,"observations":[{"actual":275,"check":"fee oracle 0","expected":275,"passed":true},{"actual":175,"check":"fee oracle 1","expected":175,"passed":true},{"actual":325,"check":"fee oracle 2","expected":300,"passed":false},{"actual":325,"check":"fee oracle 3","expected":325,"passed":true},{"actual":300,"check":"fee oracle 4","expected":300,"passed":true},{"actual":275,"check":"fee oracle 5","expected":275,"passed":true},{"actual":200,"check":"fee oracle 6","expected":200,"passed":true},{"actual":425,"check":"fee oracle 7","expected":425,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"fee oracle 0\", \"actual\": 275, \"expected\": 275, \"passed\": true}, {\"check\": \"fee oracle 1\", \"actual\": 175, \"expected\": 175, \"passed\": true}, {\"check\": \"fee oracle 2\", \"actual\": 325, \"expected\": 300, \"passed\": false}, {\"check\": \"fee oracle 3\", \"actual\": 325, \"expected\": 325, \"passed\": true}, {\"check\": \"fee oracle 4\", \"actual\": 300, \"expected\": 300, \"passed\": true}, {\"check\": \"fee oracle 5\", \"actual\": 275, \"expected\": 275, \"passed\": true}, {\"check\": \"fee oracle 6\", \"actual\": 200, \"expected\": 200, \"passed\": true}, {\"check\": \"fee oracle 7\", \"actual\": 425, \"expected\": 425, \"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."}}