FA-85356 / Ride-hailing fare and surge pricing / Open access
Base fare added on top of the minimum · case 01
A minimum-fare trip is charged the minimum plus the base fare.
ROOT CAUSE
The base fare is excluded from the subtotal compared with the minimum and added afterwards.
VERIFIED REPAIR
Include the base fare in the subtotal compared with the minimum.
Unsuccessful approach: Raising the minimum by the base fare charges the same inflated floor.
Case contract
All money is integer cents. Distance charge = meters x per_km / 1000 and time charge = seconds x per_min / 60, each rounded half up to a cent on its own. Subtotal = base + distance + time. The minimum fare applies to that subtotal only; the booking fee is added after the minimum. Return the distance charge, time charge and fare.
Why this case matters
Ride-hailing prices are computed per trip at scale; ordering, unit and boundary slips become systematic over- or under-charging.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(trip, rate):
def half_up(num, den):
return (num * 2 + den) // (den * 2)
dist = half_up(trip['meters'] * rate['per_km'], 1000)
tm = half_up(trip['seconds'] * rate['per_min'], 60)
sub = dist + tm
fare = max(sub, rate['minimum']) + rate['base'] + rate['booking_fee']
return {'distance': dist, 'time': tm, 'fare': fare}
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: base inside minimum',
[{'meters': 4, 'seconds': 1629},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 95, 'per_min': 25}],
{'distance': 0, 'fare': 1250, 'time': 679}),
('partial repair probe: base inside minimum',
[{'meters': 2500, 'seconds': 0},
{'base': 150, 'booking_fee': 250, 'minimum': 700, 'per_km': 95, 'per_min': 30}],
{'distance': 238, 'fare': 950, 'time': 0}),
('second regression',
[{'meters': 0, 'seconds': 61},
{'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 250, 'per_min': 35}],
{'distance': 0, 'fare': 750, 'time': 36}),
('normal control 1',
[{'meters': 14115, 'seconds': 90},
{'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 30}],
{'distance': 1764, 'fare': 2059, 'time': 45}),
('normal control 2',
[{'meters': 500, 'seconds': 121},
{'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 125, 'fare': 700, 'time': 71}),
('normal control 3',
[{'meters': 0, 'seconds': 61},
{'base': 0, 'booking_fee': 0, 'minimum': 1000, 'per_km': 250, 'per_min': 35}],
{'distance': 0, 'fare': 1000, 'time': 36}),
('normal control 4',
[{'meters': 6825, 'seconds': 61},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 250, 'per_min': 30}],
{'distance': 1706, 'fare': 2237, 'time': 31})],
[('regression: base inside minimum',
[{'meters': 4, 'seconds': 61},
{'base': 250, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 35}],
{'distance': 0, 'fare': 750, 'time': 36}),
('partial repair probe: base inside minimum',
[{'meters': 0, 'seconds': 61},
{'base': 150, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 35}],
{'distance': 0, 'fare': 1175, 'time': 36}),
('second regression',
[{'meters': 4, 'seconds': 0},
{'base': 150, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 35}],
{'distance': 1, 'fare': 950, 'time': 0}),
('normal control 1',
[{'meters': 21815, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 25}],
{'distance': 2727, 'fare': 3002, 'time': 25}),
('normal control 2',
[{'meters': 500, 'seconds': 0},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 250, 'per_min': 25}],
{'distance': 125, 'fare': 500, 'time': 0}),
('normal control 3',
[{'meters': 0, 'seconds': 90},
{'base': 0, 'booking_fee': 250, 'minimum': 500, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 750, 'time': 45}),
('normal control 4',
[{'meters': 12, 'seconds': 90},
{'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 30}],
{'distance': 3, 'fare': 875, 'time': 45})],
[('regression: base inside minimum',
[{'meters': 0, 'seconds': 90},
{'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 110, 'per_min': 25}],
{'distance': 0, 'fare': 750, 'time': 38}),
('partial repair probe: base inside minimum',
[{'meters': 4, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 700, 'time': 31}),
('second regression',
[{'meters': 12, 'seconds': 214},
{'base': 250, 'booking_fee': 175, 'minimum': 700, 'per_km': 95, 'per_min': 25}],
{'distance': 1, 'fare': 875, 'time': 89}),
('normal control 1',
[{'meters': 0, 'seconds': 30},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 15}],
{'distance': 0, 'fare': 950, 'time': 8}),
('normal control 2',
[{'meters': 11380, 'seconds': 30},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 2845, 'fare': 3113, 'time': 18}),
('normal control 3',
[{'meters': 14883, 'seconds': 245},
{'base': 150, 'booking_fee': 175, 'minimum': 500, 'per_km': 125, 'per_min': 30}],
{'distance': 1860, 'fare': 2308, 'time': 123}),
('normal control 4',
[{'meters': 17188, 'seconds': 0},
{'base': 250, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 35}],
{'distance': 1633, 'fare': 2133, 'time': 0})],
[('regression: base inside minimum',
[{'meters': 4, 'seconds': 61},
{'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 95, 'per_min': 30}],
{'distance': 0, 'fare': 1175, 'time': 31}),
('partial repair probe: base inside minimum',
[{'meters': 0, 'seconds': 30},
{'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 95, 'per_min': 25}],
{'distance': 0, 'fare': 950, 'time': 13}),
('second regression',
[{'meters': 1004, 'seconds': 90},
{'base': 150, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 25}],
{'distance': 110, 'fare': 500, 'time': 38}),
('normal control 1',
[{'meters': 500, 'seconds': 30},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 15}],
{'distance': 63, 'fare': 950, 'time': 8}),
('normal control 2',
[{'meters': 12, 'seconds': 0}, {'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 15}],
{'distance': 1, 'fare': 700, 'time': 0}),
('normal control 3',
[{'meters': 4, 'seconds': 0},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 1, 'fare': 950, 'time': 0}),
('normal control 4',
[{'meters': 2500, 'seconds': 0},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 625, 'fare': 950, 'time': 0})],
[('regression: base inside minimum',
[{'meters': 500, 'seconds': 30},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 110, 'per_min': 30}],
{'distance': 55, 'fare': 1250, 'time': 15}),
('partial repair probe: base inside minimum',
[{'meters': 2500, 'seconds': 30},
{'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 125, 'per_min': 25}],
{'distance': 313, 'fare': 576, 'time': 13}),
('second regression',
[{'meters': 500, 'seconds': 61},
{'base': 250, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 15}],
{'distance': 125, 'fare': 875, 'time': 15}),
('normal control 1',
[{'meters': 500, 'seconds': 1622},
{'base': 250, 'booking_fee': 175, 'minimum': 500, 'per_km': 250, 'per_min': 15}],
{'distance': 125, 'fare': 956, 'time': 406}),
('normal control 2',
[{'meters': 500, 'seconds': 90},
{'base': 0, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 15}],
{'distance': 48, 'fare': 750, 'time': 23}),
('normal control 3',
[{'meters': 15947, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 125, 'per_min': 15}],
{'distance': 1993, 'fare': 2258, 'time': 15}),
('normal control 4',
[{'meters': 2500, 'seconds': 2303},
{'base': 150, 'booking_fee': 250, 'minimum': 1000, 'per_km': 125, 'per_min': 25}],
{'distance': 313, 'fare': 1673, 'time': 960})]]
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: base inside minimum | {'distance': 0, 'fare': 1500, 'time': 679} | {'distance': 0, 'fare': 1250, 'time': 679} | Failed |
| partial repair probe: base inside minimum | {'distance': 238, 'fare': 1100, 'time': 0} | {'distance': 238, 'fare': 950, 'time': 0} | Failed |
| second regression | {'distance': 0, 'fare': 900, 'time': 36} | {'distance': 0, 'fare': 750, 'time': 36} | Failed |
| normal control 1 | {'distance': 1764, 'fare': 2059, 'time': 45} | {'distance': 1764, 'fare': 2059, 'time': 45} | Passed |
| normal control 2 | {'distance': 125, 'fare': 700, 'time': 71} | {'distance': 125, 'fare': 700, 'time': 71} | Passed |
| normal control 3 | {'distance': 0, 'fare': 1000, 'time': 36} | {'distance': 0, 'fare': 1000, 'time': 36} | Passed |
| normal control 4 | {'distance': 1706, 'fare': 2237, 'time': 31} | {'distance': 1706, 'fare': 2237, 'time': 31} | Passed |
SHA-256 / 8037704722afe759c656edd107e46f972fc3a890a8e8e9eceba92a5ccedcb1db
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(trip, rate):
def half_up(num, den):
return (num * 2 + den) // (den * 2)
dist = half_up(trip['meters'] * rate['per_km'], 1000)
tm = half_up(trip['seconds'] * rate['per_min'], 60)
sub = rate['base'] + dist + tm
fare = max(sub, rate['minimum'] + rate['base']) + rate['booking_fee']
return {'distance': dist, 'time': tm, 'fare': fare}
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: base inside minimum',
[{'meters': 4, 'seconds': 1629},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 95, 'per_min': 25}],
{'distance': 0, 'fare': 1250, 'time': 679}),
('partial repair probe: base inside minimum',
[{'meters': 2500, 'seconds': 0},
{'base': 150, 'booking_fee': 250, 'minimum': 700, 'per_km': 95, 'per_min': 30}],
{'distance': 238, 'fare': 950, 'time': 0}),
('second regression',
[{'meters': 0, 'seconds': 61},
{'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 250, 'per_min': 35}],
{'distance': 0, 'fare': 750, 'time': 36}),
('normal control 1',
[{'meters': 14115, 'seconds': 90},
{'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 30}],
{'distance': 1764, 'fare': 2059, 'time': 45}),
('normal control 2',
[{'meters': 500, 'seconds': 121},
{'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 125, 'fare': 700, 'time': 71}),
('normal control 3',
[{'meters': 0, 'seconds': 61},
{'base': 0, 'booking_fee': 0, 'minimum': 1000, 'per_km': 250, 'per_min': 35}],
{'distance': 0, 'fare': 1000, 'time': 36}),
('normal control 4',
[{'meters': 6825, 'seconds': 61},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 250, 'per_min': 30}],
{'distance': 1706, 'fare': 2237, 'time': 31})],
[('regression: base inside minimum',
[{'meters': 4, 'seconds': 61},
{'base': 250, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 35}],
{'distance': 0, 'fare': 750, 'time': 36}),
('partial repair probe: base inside minimum',
[{'meters': 0, 'seconds': 61},
{'base': 150, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 35}],
{'distance': 0, 'fare': 1175, 'time': 36}),
('second regression',
[{'meters': 4, 'seconds': 0},
{'base': 150, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 35}],
{'distance': 1, 'fare': 950, 'time': 0}),
('normal control 1',
[{'meters': 21815, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 25}],
{'distance': 2727, 'fare': 3002, 'time': 25}),
('normal control 2',
[{'meters': 500, 'seconds': 0},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 250, 'per_min': 25}],
{'distance': 125, 'fare': 500, 'time': 0}),
('normal control 3',
[{'meters': 0, 'seconds': 90},
{'base': 0, 'booking_fee': 250, 'minimum': 500, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 750, 'time': 45}),
('normal control 4',
[{'meters': 12, 'seconds': 90},
{'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 30}],
{'distance': 3, 'fare': 875, 'time': 45})],
[('regression: base inside minimum',
[{'meters': 0, 'seconds': 90},
{'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 110, 'per_min': 25}],
{'distance': 0, 'fare': 750, 'time': 38}),
('partial repair probe: base inside minimum',
[{'meters': 4, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 700, 'time': 31}),
('second regression',
[{'meters': 12, 'seconds': 214},
{'base': 250, 'booking_fee': 175, 'minimum': 700, 'per_km': 95, 'per_min': 25}],
{'distance': 1, 'fare': 875, 'time': 89}),
('normal control 1',
[{'meters': 0, 'seconds': 30},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 15}],
{'distance': 0, 'fare': 950, 'time': 8}),
('normal control 2',
[{'meters': 11380, 'seconds': 30},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 2845, 'fare': 3113, 'time': 18}),
('normal control 3',
[{'meters': 14883, 'seconds': 245},
{'base': 150, 'booking_fee': 175, 'minimum': 500, 'per_km': 125, 'per_min': 30}],
{'distance': 1860, 'fare': 2308, 'time': 123}),
('normal control 4',
[{'meters': 17188, 'seconds': 0},
{'base': 250, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 35}],
{'distance': 1633, 'fare': 2133, 'time': 0})],
[('regression: base inside minimum',
[{'meters': 4, 'seconds': 61},
{'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 95, 'per_min': 30}],
{'distance': 0, 'fare': 1175, 'time': 31}),
('partial repair probe: base inside minimum',
[{'meters': 0, 'seconds': 30},
{'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 95, 'per_min': 25}],
{'distance': 0, 'fare': 950, 'time': 13}),
('second regression',
[{'meters': 1004, 'seconds': 90},
{'base': 150, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 25}],
{'distance': 110, 'fare': 500, 'time': 38}),
('normal control 1',
[{'meters': 500, 'seconds': 30},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 15}],
{'distance': 63, 'fare': 950, 'time': 8}),
('normal control 2',
[{'meters': 12, 'seconds': 0}, {'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 15}],
{'distance': 1, 'fare': 700, 'time': 0}),
('normal control 3',
[{'meters': 4, 'seconds': 0},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 1, 'fare': 950, 'time': 0}),
('normal control 4',
[{'meters': 2500, 'seconds': 0},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 625, 'fare': 950, 'time': 0})],
[('regression: base inside minimum',
[{'meters': 500, 'seconds': 30},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 110, 'per_min': 30}],
{'distance': 55, 'fare': 1250, 'time': 15}),
('partial repair probe: base inside minimum',
[{'meters': 2500, 'seconds': 30},
{'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 125, 'per_min': 25}],
{'distance': 313, 'fare': 576, 'time': 13}),
('second regression',
[{'meters': 500, 'seconds': 61},
{'base': 250, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 15}],
{'distance': 125, 'fare': 875, 'time': 15}),
('normal control 1',
[{'meters': 500, 'seconds': 1622},
{'base': 250, 'booking_fee': 175, 'minimum': 500, 'per_km': 250, 'per_min': 15}],
{'distance': 125, 'fare': 956, 'time': 406}),
('normal control 2',
[{'meters': 500, 'seconds': 90},
{'base': 0, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 15}],
{'distance': 48, 'fare': 750, 'time': 23}),
('normal control 3',
[{'meters': 15947, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 125, 'per_min': 15}],
{'distance': 1993, 'fare': 2258, 'time': 15}),
('normal control 4',
[{'meters': 2500, 'seconds': 2303},
{'base': 150, 'booking_fee': 250, 'minimum': 1000, 'per_km': 125, 'per_min': 25}],
{'distance': 313, 'fare': 1673, 'time': 960})]]
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: base inside minimum | {'distance': 0, 'fare': 1500, 'time': 679} | {'distance': 0, 'fare': 1250, 'time': 679} | Failed |
| partial repair probe: base inside minimum | {'distance': 238, 'fare': 1100, 'time': 0} | {'distance': 238, 'fare': 950, 'time': 0} | Failed |
| second regression | {'distance': 0, 'fare': 900, 'time': 36} | {'distance': 0, 'fare': 750, 'time': 36} | Failed |
| normal control 1 | {'distance': 1764, 'fare': 2059, 'time': 45} | {'distance': 1764, 'fare': 2059, 'time': 45} | Passed |
| normal control 2 | {'distance': 125, 'fare': 700, 'time': 71} | {'distance': 125, 'fare': 700, 'time': 71} | Passed |
| normal control 3 | {'distance': 0, 'fare': 1000, 'time': 36} | {'distance': 0, 'fare': 1000, 'time': 36} | Passed |
| normal control 4 | {'distance': 1706, 'fare': 2237, 'time': 31} | {'distance': 1706, 'fare': 2237, 'time': 31} | Passed |
SHA-256 / 6a441771108d7671295a8f0db02d56c45757d60eb6caa1009770aa25395dad1e
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(trip, rate):
def half_up(num, den):
return (num * 2 + den) // (den * 2)
dist = half_up(trip['meters'] * rate['per_km'], 1000)
tm = half_up(trip['seconds'] * rate['per_min'], 60)
sub = rate['base'] + dist + tm
fare = max(sub, rate['minimum']) + rate['booking_fee']
return {'distance': dist, 'time': tm, 'fare': fare}
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: base inside minimum',
[{'meters': 4, 'seconds': 1629},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 95, 'per_min': 25}],
{'distance': 0, 'fare': 1250, 'time': 679}),
('partial repair probe: base inside minimum',
[{'meters': 2500, 'seconds': 0},
{'base': 150, 'booking_fee': 250, 'minimum': 700, 'per_km': 95, 'per_min': 30}],
{'distance': 238, 'fare': 950, 'time': 0}),
('second regression',
[{'meters': 0, 'seconds': 61},
{'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 250, 'per_min': 35}],
{'distance': 0, 'fare': 750, 'time': 36}),
('normal control 1',
[{'meters': 14115, 'seconds': 90},
{'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 30}],
{'distance': 1764, 'fare': 2059, 'time': 45}),
('normal control 2',
[{'meters': 500, 'seconds': 121},
{'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 125, 'fare': 700, 'time': 71}),
('normal control 3',
[{'meters': 0, 'seconds': 61},
{'base': 0, 'booking_fee': 0, 'minimum': 1000, 'per_km': 250, 'per_min': 35}],
{'distance': 0, 'fare': 1000, 'time': 36}),
('normal control 4',
[{'meters': 6825, 'seconds': 61},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 250, 'per_min': 30}],
{'distance': 1706, 'fare': 2237, 'time': 31})],
[('regression: base inside minimum',
[{'meters': 4, 'seconds': 61},
{'base': 250, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 35}],
{'distance': 0, 'fare': 750, 'time': 36}),
('partial repair probe: base inside minimum',
[{'meters': 0, 'seconds': 61},
{'base': 150, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 35}],
{'distance': 0, 'fare': 1175, 'time': 36}),
('second regression',
[{'meters': 4, 'seconds': 0},
{'base': 150, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 35}],
{'distance': 1, 'fare': 950, 'time': 0}),
('normal control 1',
[{'meters': 21815, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 25}],
{'distance': 2727, 'fare': 3002, 'time': 25}),
('normal control 2',
[{'meters': 500, 'seconds': 0},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 250, 'per_min': 25}],
{'distance': 125, 'fare': 500, 'time': 0}),
('normal control 3',
[{'meters': 0, 'seconds': 90},
{'base': 0, 'booking_fee': 250, 'minimum': 500, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 750, 'time': 45}),
('normal control 4',
[{'meters': 12, 'seconds': 90},
{'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 30}],
{'distance': 3, 'fare': 875, 'time': 45})],
[('regression: base inside minimum',
[{'meters': 0, 'seconds': 90},
{'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 110, 'per_min': 25}],
{'distance': 0, 'fare': 750, 'time': 38}),
('partial repair probe: base inside minimum',
[{'meters': 4, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 700, 'time': 31}),
('second regression',
[{'meters': 12, 'seconds': 214},
{'base': 250, 'booking_fee': 175, 'minimum': 700, 'per_km': 95, 'per_min': 25}],
{'distance': 1, 'fare': 875, 'time': 89}),
('normal control 1',
[{'meters': 0, 'seconds': 30},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 15}],
{'distance': 0, 'fare': 950, 'time': 8}),
('normal control 2',
[{'meters': 11380, 'seconds': 30},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 2845, 'fare': 3113, 'time': 18}),
('normal control 3',
[{'meters': 14883, 'seconds': 245},
{'base': 150, 'booking_fee': 175, 'minimum': 500, 'per_km': 125, 'per_min': 30}],
{'distance': 1860, 'fare': 2308, 'time': 123}),
('normal control 4',
[{'meters': 17188, 'seconds': 0},
{'base': 250, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 35}],
{'distance': 1633, 'fare': 2133, 'time': 0})],
[('regression: base inside minimum',
[{'meters': 4, 'seconds': 61},
{'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 95, 'per_min': 30}],
{'distance': 0, 'fare': 1175, 'time': 31}),
('partial repair probe: base inside minimum',
[{'meters': 0, 'seconds': 30},
{'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 95, 'per_min': 25}],
{'distance': 0, 'fare': 950, 'time': 13}),
('second regression',
[{'meters': 1004, 'seconds': 90},
{'base': 150, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 25}],
{'distance': 110, 'fare': 500, 'time': 38}),
('normal control 1',
[{'meters': 500, 'seconds': 30},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 15}],
{'distance': 63, 'fare': 950, 'time': 8}),
('normal control 2',
[{'meters': 12, 'seconds': 0}, {'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 15}],
{'distance': 1, 'fare': 700, 'time': 0}),
('normal control 3',
[{'meters': 4, 'seconds': 0},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 1, 'fare': 950, 'time': 0}),
('normal control 4',
[{'meters': 2500, 'seconds': 0},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 625, 'fare': 950, 'time': 0})],
[('regression: base inside minimum',
[{'meters': 500, 'seconds': 30},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 110, 'per_min': 30}],
{'distance': 55, 'fare': 1250, 'time': 15}),
('partial repair probe: base inside minimum',
[{'meters': 2500, 'seconds': 30},
{'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 125, 'per_min': 25}],
{'distance': 313, 'fare': 576, 'time': 13}),
('second regression',
[{'meters': 500, 'seconds': 61},
{'base': 250, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 15}],
{'distance': 125, 'fare': 875, 'time': 15}),
('normal control 1',
[{'meters': 500, 'seconds': 1622},
{'base': 250, 'booking_fee': 175, 'minimum': 500, 'per_km': 250, 'per_min': 15}],
{'distance': 125, 'fare': 956, 'time': 406}),
('normal control 2',
[{'meters': 500, 'seconds': 90},
{'base': 0, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 15}],
{'distance': 48, 'fare': 750, 'time': 23}),
('normal control 3',
[{'meters': 15947, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 125, 'per_min': 15}],
{'distance': 1993, 'fare': 2258, 'time': 15}),
('normal control 4',
[{'meters': 2500, 'seconds': 2303},
{'base': 150, 'booking_fee': 250, 'minimum': 1000, 'per_km': 125, 'per_min': 25}],
{'distance': 313, 'fare': 1673, 'time': 960})]]
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: base inside minimum | {'distance': 0, 'fare': 1250, 'time': 679} | {'distance': 0, 'fare': 1250, 'time': 679} | Passed |
| partial repair probe: base inside minimum | {'distance': 238, 'fare': 950, 'time': 0} | {'distance': 238, 'fare': 950, 'time': 0} | Passed |
| second regression | {'distance': 0, 'fare': 750, 'time': 36} | {'distance': 0, 'fare': 750, 'time': 36} | Passed |
| normal control 1 | {'distance': 1764, 'fare': 2059, 'time': 45} | {'distance': 1764, 'fare': 2059, 'time': 45} | Passed |
| normal control 2 | {'distance': 125, 'fare': 700, 'time': 71} | {'distance': 125, 'fare': 700, 'time': 71} | Passed |
| normal control 3 | {'distance': 0, 'fare': 1000, 'time': 36} | {'distance': 0, 'fare': 1000, 'time': 36} | Passed |
| normal control 4 | {'distance': 1706, 'fare': 2237, 'time': 31} | {'distance': 1706, 'fare': 2237, 'time': 31} | Passed |
SHA-256 / 20188a446cf404cfea6b60744a2203656a9bfed8db7cc895c63e88ffd6cf3dcb
Verification & scope
A deterministic toy pricing contract stipulated for this example; it does not reproduce the pricing of any real ride-hailing operator or regulator. 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 2026-09-29T14:50:39.640673+00:00.
Case digest / 0318453116681593195f1dd3b60e7d241f00daf65a4efa275f9a344c975834dc