{"abstract":"Demand-responsive meter rate adjustment returns a wrong result when unchanged rates are not clamped.","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].","evaluation_group":"w2-toll_and_parking_fee_computation-demand-meter-rate","failed_approach":"Skipping the clamp for unchanged rates keeps an out-of-range legacy rate.","family":"w2-toll_and_parking_fee_computation-demand-meter-rate-clamp","id":"FA-68771","implementations":{"attempt":{"sha256":"649f65f06dc27c1b0eb3adbbcd70d2b6aa2852a3dddde1aed82f65ff9269f17f","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)) if r != x['rate'] else x['rate']\ndef check(label, actual, expected):\n    observations.append({\"check\": label, \"actual\": actual, \"expected\": expected, \"passed\": actual == expected})\nfixtures = [[({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, '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': 25, 'occupancy': [59, 81, 60, 80, 81, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 50), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 50, 'occupancy': [80, 60, 81, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100), ({'rate': 75, 'occupancy': [60, 59, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100), ({'rate': 350, 'occupancy': [81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 350), ({'rate': 550, 'occupancy': [59, 59, 80, 90, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 550)], [({'rate': 125, 'occupancy': [59, 87], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 125), ({'rate': 25, 'occupancy': [80, 45, 59, 59, 59, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 50), ({'rate': 800, 'occupancy': [60, 80, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 75, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, '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': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 50, 'occupancy': [80, 81, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100)], [({'rate': 200, 'occupancy': [80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 200), ({'rate': 75, 'occupancy': [96, 60, 81, 59, 59, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 100), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 25, 'occupancy': [54, 42, 60, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 50), ({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 650, 'occupancy': [80, 59, 60, 81, 59, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 600), ({'rate': 75, 'occupancy': [81, 80, 80, 81, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100)], [({'rate': 275, 'occupancy': [54, 59, 81, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 275), ({'rate': 625, 'occupancy': [80, 85, 80, 60, 74], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 25, 'occupancy': [80, 80, 60, 74, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 50), ({'rate': 75, 'occupancy': [81, 72], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100), ({'rate': 25, 'occupancy': [52, 81, 60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'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, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'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, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 650, 'occupancy': [68, 80, 80, 75, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 25, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100), ({'rate': 475, 'occupancy': [80, 81, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 525), ({'rate': 25, 'occupancy': [59, 60, 59, 60, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 50), ({'rate': 25, 'occupancy': [81, 81, 81, 59, 59], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100)]]\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":"ae8e973b34dee5a8b61afe7f3d90022c5ee0213716c84b0e78902a40ee89c380","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 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': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, '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': 25, 'occupancy': [59, 81, 60, 80, 81, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 50), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 50, 'occupancy': [80, 60, 81, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100), ({'rate': 75, 'occupancy': [60, 59, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100), ({'rate': 350, 'occupancy': [81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 350), ({'rate': 550, 'occupancy': [59, 59, 80, 90, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 550)], [({'rate': 125, 'occupancy': [59, 87], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 125), ({'rate': 25, 'occupancy': [80, 45, 59, 59, 59, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 50), ({'rate': 800, 'occupancy': [60, 80, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 75, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, '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': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 50, 'occupancy': [80, 81, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100)], [({'rate': 200, 'occupancy': [80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 200), ({'rate': 75, 'occupancy': [96, 60, 81, 59, 59, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 100), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 25, 'occupancy': [54, 42, 60, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 50), ({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 650, 'occupancy': [80, 59, 60, 81, 59, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 600), ({'rate': 75, 'occupancy': [81, 80, 80, 81, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100)], [({'rate': 275, 'occupancy': [54, 59, 81, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 275), ({'rate': 625, 'occupancy': [80, 85, 80, 60, 74], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 25, 'occupancy': [80, 80, 60, 74, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 50), ({'rate': 75, 'occupancy': [81, 72], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100), ({'rate': 25, 'occupancy': [52, 81, 60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'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, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'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, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 650, 'occupancy': [68, 80, 80, 75, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 25, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100), ({'rate': 475, 'occupancy': [80, 81, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 525), ({'rate': 25, 'occupancy': [59, 60, 59, 60, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 50), ({'rate': 25, 'occupancy': [81, 81, 81, 59, 59], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100)]]\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"},"fixed":{"sha256":"849cbdfa6f5199a3c0785911705e341b7db81b7eba3bede030ecf4b39840145d","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': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, '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': 25, 'occupancy': [59, 81, 60, 80, 81, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 50), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 50, 'occupancy': [80, 60, 81, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100), ({'rate': 75, 'occupancy': [60, 59, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100), ({'rate': 350, 'occupancy': [81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 350), ({'rate': 550, 'occupancy': [59, 59, 80, 90, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 550)], [({'rate': 125, 'occupancy': [59, 87], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 3}, 125), ({'rate': 25, 'occupancy': [80, 45, 59, 59, 59, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 50), ({'rate': 800, 'occupancy': [60, 80, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 75, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, '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': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 300, 'occupancy': [60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 50, 'occupancy': [80, 81, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 100)], [({'rate': 200, 'occupancy': [80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 200), ({'rate': 75, 'occupancy': [96, 60, 81, 59, 59, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 3}, 100), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 300, 'occupancy': [80, 80], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 300), ({'rate': 25, 'occupancy': [54, 42, 60, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 50), ({'rate': 300, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'rate': 650, 'occupancy': [80, 59, 60, 81, 59, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 600), ({'rate': 75, 'occupancy': [81, 80, 80, 81, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100)], [({'rate': 275, 'occupancy': [54, 59, 81, 60], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 275), ({'rate': 625, 'occupancy': [80, 85, 80, 60, 74], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 25, 'occupancy': [80, 80, 60, 74, 80], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 50), ({'rate': 75, 'occupancy': [81, 72], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100), ({'rate': 25, 'occupancy': [52, 81, 60, 60, 60], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100), ({'rate': 300, 'occupancy': [80, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'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, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 275), ({'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, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 2}, 325), ({'rate': 650, 'occupancy': [68, 80, 80, 75, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 600, 'min_samples': 3}, 600), ({'rate': 25, 'occupancy': [60, 59], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100), ({'rate': 475, 'occupancy': [80, 81, 81], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 700, 'min_samples': 2}, 525), ({'rate': 25, 'occupancy': [59, 60, 59, 60, 81], 'hi': 80, 'lo': 60, 'step': 25, 'min_rate': 50, 'max_rate': 700, 'min_samples': 2}, 50), ({'rate': 25, 'occupancy': [81, 81, 81, 59, 59], 'hi': 80, 'lo': 60, 'step': 50, 'min_rate': 100, 'max_rate': 600, 'min_samples': 2}, 100)]]\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-clamp","generated_at":"2026-09-29T14:48:05.417748+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.","repair":"Restore the rate bounds rule so that the step reads `max(x['min_rate'], min(x['max_rate'], r))`.","root_cause":"Decreases can push the rate below the minimum.","sha256":"f14a63ea9635fe4b67adff5418a556d5409e594d53472f36e68f82d29b88d0d1","title":"Demand-responsive meter rate adjustment: unchanged rates are not clamped · case 01","variant":1,"variant_policy":"Five numbered records share a model and may reuse boundary fixtures.","verification":{"attempt":{"elapsed_ms":47.098,"exit_code":1,"observations":[{"actual":300,"check":"fee oracle 0","expected":300,"passed":true},{"actual":800,"check":"fee oracle 1","expected":600,"passed":false},{"actual":25,"check":"fee oracle 2","expected":50,"passed":false},{"actual":325,"check":"fee oracle 3","expected":325,"passed":true},{"actual":50,"check":"fee oracle 4","expected":100,"passed":false},{"actual":75,"check":"fee oracle 5","expected":100,"passed":false},{"actual":350,"check":"fee oracle 6","expected":350,"passed":true},{"actual":550,"check":"fee oracle 7","expected":550,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"fee oracle 0\", \"actual\": 300, \"expected\": 300, \"passed\": true}, {\"check\": \"fee oracle 1\", \"actual\": 800, \"expected\": 600, \"passed\": false}, {\"check\": \"fee oracle 2\", \"actual\": 25, \"expected\": 50, \"passed\": false}, {\"check\": \"fee oracle 3\", \"actual\": 325, \"expected\": 325, \"passed\": true}, {\"check\": \"fee oracle 4\", \"actual\": 50, \"expected\": 100, \"passed\": false}, {\"check\": \"fee oracle 5\", \"actual\": 75, \"expected\": 100, \"passed\": false}, {\"check\": \"fee oracle 6\", \"actual\": 350, \"expected\": 350, \"passed\": true}, {\"check\": \"fee oracle 7\", \"actual\": 550, \"expected\": 550, \"passed\": true}], \"passed\": false}\n"},"broken":{"elapsed_ms":46.011,"exit_code":1,"observations":[{"actual":300,"check":"fee oracle 0","expected":300,"passed":true},{"actual":600,"check":"fee oracle 1","expected":600,"passed":true},{"actual":25,"check":"fee oracle 2","expected":50,"passed":false},{"actual":325,"check":"fee oracle 3","expected":325,"passed":true},{"actual":50,"check":"fee oracle 4","expected":100,"passed":false},{"actual":75,"check":"fee oracle 5","expected":100,"passed":false},{"actual":350,"check":"fee oracle 6","expected":350,"passed":true},{"actual":550,"check":"fee oracle 7","expected":550,"passed":true}],"passed":false,"stderr":"","stdout":"{\"observations\": [{\"check\": \"fee oracle 0\", \"actual\": 300, \"expected\": 300, \"passed\": true}, {\"check\": \"fee oracle 1\", \"actual\": 600, \"expected\": 600, \"passed\": true}, {\"check\": \"fee oracle 2\", \"actual\": 25, \"expected\": 50, \"passed\": false}, {\"check\": \"fee oracle 3\", \"actual\": 325, \"expected\": 325, \"passed\": true}, {\"check\": \"fee oracle 4\", \"actual\": 50, \"expected\": 100, \"passed\": false}, {\"check\": \"fee oracle 5\", \"actual\": 75, \"expected\": 100, \"passed\": false}, {\"check\": \"fee oracle 6\", \"actual\": 350, \"expected\": 350, \"passed\": true}, {\"check\": \"fee oracle 7\", \"actual\": 550, \"expected\": 550, \"passed\": true}], \"passed\": false}\n"},"fixed":{"elapsed_ms":45.225,"exit_code":0,"observations":[{"actual":300,"check":"fee oracle 0","expected":300,"passed":true},{"actual":600,"check":"fee oracle 1","expected":600,"passed":true},{"actual":50,"check":"fee oracle 2","expected":50,"passed":true},{"actual":325,"check":"fee oracle 3","expected":325,"passed":true},{"actual":100,"check":"fee oracle 4","expected":100,"passed":true},{"actual":100,"check":"fee oracle 5","expected":100,"passed":true},{"actual":350,"check":"fee oracle 6","expected":350,"passed":true},{"actual":550,"check":"fee oracle 7","expected":550,"passed":true}],"passed":true,"stderr":"","stdout":"{\"observations\": [{\"check\": \"fee oracle 0\", \"actual\": 300, \"expected\": 300, \"passed\": true}, {\"check\": \"fee oracle 1\", \"actual\": 600, \"expected\": 600, \"passed\": true}, {\"check\": \"fee oracle 2\", \"actual\": 50, \"expected\": 50, \"passed\": true}, {\"check\": \"fee oracle 3\", \"actual\": 325, \"expected\": 325, \"passed\": true}, {\"check\": \"fee oracle 4\", \"actual\": 100, \"expected\": 100, \"passed\": true}, {\"check\": \"fee oracle 5\", \"actual\": 100, \"expected\": 100, \"passed\": true}, {\"check\": \"fee oracle 6\", \"actual\": 350, \"expected\": 350, \"passed\": true}, {\"check\": \"fee oracle 7\", \"actual\": 550, \"expected\": 550, \"passed\": true}], \"passed\": true}\n"}},"verified":true,"visibility":"public"}