FA-85341 / Ride-hailing fare and surge pricing / Open access
Booking fee absorbed into the minimum fare · case 01
Short trips charged exactly the minimum fare, missing the booking fee.
ROOT CAUSE
The minimum is compared after adding the booking fee, so the fee disappears into the minimum.
VERIFIED REPAIR
Apply the minimum to the metered subtotal, then add the booking fee.
Unsuccessful approach: Dropping the fee whenever the minimum applies still omits it on the short trips it was meant for.
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 = rate['base'] + dist + tm
fare = max(sub + rate['booking_fee'], rate['minimum'])
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: minimum before booking fee',
[{'meters': 0, 'seconds': 30},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 125, 'per_min': 35}],
{'distance': 0, 'fare': 1250, 'time': 18}),
('partial repair probe: minimum before booking fee',
[{'meters': 4, 'seconds': 30},
{'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 30}],
{'distance': 0, 'fare': 750, 'time': 15}),
('second regression',
[{'meters': 0, 'seconds': 30},
{'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 875, 'time': 15}),
('normal control 1',
[{'meters': 2500, 'seconds': 30},
{'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 15}],
{'distance': 313, 'fare': 1000, 'time': 8}),
('normal control 2',
[{'meters': 0, 'seconds': 61},
{'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 125, 'per_min': 15}],
{'distance': 0, 'fare': 700, 'time': 15}),
('normal control 3',
[{'meters': 500, 'seconds': 0},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 250, 'per_min': 30}],
{'distance': 125, 'fare': 500, 'time': 0}),
('normal control 4',
[{'meters': 0, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 35}],
{'distance': 0, 'fare': 500, 'time': 36})],
[('regression: minimum before booking fee',
[{'meters': 2500, 'seconds': 90},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 15}],
{'distance': 313, 'fare': 950, 'time': 23}),
('partial repair probe: minimum before booking fee',
[{'meters': 0, 'seconds': 0},
{'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 95, 'per_min': 35}],
{'distance': 0, 'fare': 1175, 'time': 0}),
('second regression',
[{'meters': 0, 'seconds': 90},
{'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 1175, 'time': 45}),
('normal control 1',
[{'meters': 15327, 'seconds': 30},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 30}],
{'distance': 1916, 'fare': 2181, 'time': 15}),
('normal control 2',
[{'meters': 500, 'seconds': 1882},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 250, 'per_min': 25}],
{'distance': 125, 'fare': 909, 'time': 784}),
('normal control 3',
[{'meters': 500, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 30}],
{'distance': 48, 'fare': 700, 'time': 31}),
('normal control 4',
[{'meters': 500, 'seconds': 0},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 35}],
{'distance': 48, 'fare': 700, 'time': 0})],
[('regression: minimum before booking fee',
[{'meters': 1004, 'seconds': 0},
{'base': 0, 'booking_fee': 175, 'minimum': 500, 'per_km': 250, 'per_min': 15}],
{'distance': 251, 'fare': 675, 'time': 0}),
('partial repair probe: minimum before booking fee',
[{'meters': 4, 'seconds': 0},
{'base': 150, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 950, 'time': 0}),
('second regression',
[{'meters': 12, 'seconds': 90},
{'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 30}],
{'distance': 1, 'fare': 950, 'time': 45}),
('normal control 1',
[{'meters': 0, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 15}],
{'distance': 0, 'fare': 500, 'time': 15}),
('normal control 2',
[{'meters': 6354, 'seconds': 30},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 35}],
{'distance': 699, 'fare': 717, 'time': 18}),
('normal control 3',
[{'meters': 4, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 95, 'per_min': 35}],
{'distance': 0, 'fare': 500, 'time': 36}),
('normal control 4',
[{'meters': 23665, 'seconds': 30},
{'base': 0, 'booking_fee': 175, 'minimum': 500, 'per_km': 110, 'per_min': 15}],
{'distance': 2603, 'fare': 2786, 'time': 8})],
[('regression: minimum before booking fee',
[{'meters': 500, 'seconds': 30},
{'base': 150, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 15}],
{'distance': 55, 'fare': 1175, 'time': 8}),
('partial repair probe: minimum before booking fee',
[{'meters': 0, 'seconds': 1374},
{'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 110, 'per_min': 15}],
{'distance': 0, 'fare': 875, 'time': 344}),
('second regression',
[{'meters': 500, 'seconds': 30},
{'base': 0, 'booking_fee': 175, 'minimum': 500, 'per_km': 110, 'per_min': 35}],
{'distance': 55, 'fare': 675, 'time': 18}),
('normal control 1',
[{'meters': 1004, 'seconds': 61},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 95, 'per_min': 15}],
{'distance': 95, 'fare': 500, 'time': 15}),
('normal control 2',
[{'meters': 0, 'seconds': 30},
{'base': 150, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 30}],
{'distance': 0, 'fare': 1000, 'time': 15}),
('normal control 3',
[{'meters': 12, 'seconds': 61},
{'base': 150, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 15}],
{'distance': 1, 'fare': 500, 'time': 15}),
('normal control 4',
[{'meters': 24766, 'seconds': 30},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 250, 'per_min': 30}],
{'distance': 6192, 'fare': 6707, 'time': 15})],
[('regression: minimum before booking fee',
[{'meters': 500, 'seconds': 61},
{'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 125, 'per_min': 35}],
{'distance': 63, 'fare': 750, 'time': 36}),
('partial repair probe: minimum before booking fee',
[{'meters': 500, 'seconds': 90},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 35}],
{'distance': 55, 'fare': 950, 'time': 53}),
('second regression',
[{'meters': 2500, 'seconds': 30},
{'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 15}],
{'distance': 625, 'fare': 875, 'time': 8}),
('normal control 1',
[{'meters': 4, 'seconds': 2383},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 1, 'fare': 1641, 'time': 1390}),
('normal control 2',
[{'meters': 0, 'seconds': 1117},
{'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 30}],
{'distance': 0, 'fare': 1059, 'time': 559}),
('normal control 3',
[{'meters': 0, 'seconds': 30}, {'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 35}],
{'distance': 0, 'fare': 700, 'time': 18}),
('normal control 4',
[{'meters': 12, 'seconds': 30},
{'base': 150, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 15}],
{'distance': 1, 'fare': 500, 'time': 8})]]
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: minimum before booking fee | {'distance': 0, 'fare': 1000, 'time': 18} | {'distance': 0, 'fare': 1250, 'time': 18} | Failed |
| partial repair probe: minimum before booking fee | {'distance': 0, 'fare': 500, 'time': 15} | {'distance': 0, 'fare': 750, 'time': 15} | Failed |
| second regression | {'distance': 0, 'fare': 700, 'time': 15} | {'distance': 0, 'fare': 875, 'time': 15} | Failed |
| normal control 1 | {'distance': 313, 'fare': 1000, 'time': 8} | {'distance': 313, 'fare': 1000, 'time': 8} | Passed |
| normal control 2 | {'distance': 0, 'fare': 700, 'time': 15} | {'distance': 0, 'fare': 700, 'time': 15} | Passed |
| normal control 3 | {'distance': 125, 'fare': 500, 'time': 0} | {'distance': 125, 'fare': 500, 'time': 0} | Passed |
| normal control 4 | {'distance': 0, 'fare': 500, 'time': 36} | {'distance': 0, 'fare': 500, 'time': 36} | Passed |
SHA-256 / 657791282608213df8531d4837d43bc378231655b5c44a94d58a301553289045
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['booking_fee'] if sub >= rate['minimum'] else 0)
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: minimum before booking fee',
[{'meters': 0, 'seconds': 30},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 125, 'per_min': 35}],
{'distance': 0, 'fare': 1250, 'time': 18}),
('partial repair probe: minimum before booking fee',
[{'meters': 4, 'seconds': 30},
{'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 30}],
{'distance': 0, 'fare': 750, 'time': 15}),
('second regression',
[{'meters': 0, 'seconds': 30},
{'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 875, 'time': 15}),
('normal control 1',
[{'meters': 2500, 'seconds': 30},
{'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 15}],
{'distance': 313, 'fare': 1000, 'time': 8}),
('normal control 2',
[{'meters': 0, 'seconds': 61},
{'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 125, 'per_min': 15}],
{'distance': 0, 'fare': 700, 'time': 15}),
('normal control 3',
[{'meters': 500, 'seconds': 0},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 250, 'per_min': 30}],
{'distance': 125, 'fare': 500, 'time': 0}),
('normal control 4',
[{'meters': 0, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 35}],
{'distance': 0, 'fare': 500, 'time': 36})],
[('regression: minimum before booking fee',
[{'meters': 2500, 'seconds': 90},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 15}],
{'distance': 313, 'fare': 950, 'time': 23}),
('partial repair probe: minimum before booking fee',
[{'meters': 0, 'seconds': 0},
{'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 95, 'per_min': 35}],
{'distance': 0, 'fare': 1175, 'time': 0}),
('second regression',
[{'meters': 0, 'seconds': 90},
{'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 1175, 'time': 45}),
('normal control 1',
[{'meters': 15327, 'seconds': 30},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 30}],
{'distance': 1916, 'fare': 2181, 'time': 15}),
('normal control 2',
[{'meters': 500, 'seconds': 1882},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 250, 'per_min': 25}],
{'distance': 125, 'fare': 909, 'time': 784}),
('normal control 3',
[{'meters': 500, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 30}],
{'distance': 48, 'fare': 700, 'time': 31}),
('normal control 4',
[{'meters': 500, 'seconds': 0},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 35}],
{'distance': 48, 'fare': 700, 'time': 0})],
[('regression: minimum before booking fee',
[{'meters': 1004, 'seconds': 0},
{'base': 0, 'booking_fee': 175, 'minimum': 500, 'per_km': 250, 'per_min': 15}],
{'distance': 251, 'fare': 675, 'time': 0}),
('partial repair probe: minimum before booking fee',
[{'meters': 4, 'seconds': 0},
{'base': 150, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 950, 'time': 0}),
('second regression',
[{'meters': 12, 'seconds': 90},
{'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 30}],
{'distance': 1, 'fare': 950, 'time': 45}),
('normal control 1',
[{'meters': 0, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 15}],
{'distance': 0, 'fare': 500, 'time': 15}),
('normal control 2',
[{'meters': 6354, 'seconds': 30},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 35}],
{'distance': 699, 'fare': 717, 'time': 18}),
('normal control 3',
[{'meters': 4, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 95, 'per_min': 35}],
{'distance': 0, 'fare': 500, 'time': 36}),
('normal control 4',
[{'meters': 23665, 'seconds': 30},
{'base': 0, 'booking_fee': 175, 'minimum': 500, 'per_km': 110, 'per_min': 15}],
{'distance': 2603, 'fare': 2786, 'time': 8})],
[('regression: minimum before booking fee',
[{'meters': 500, 'seconds': 30},
{'base': 150, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 15}],
{'distance': 55, 'fare': 1175, 'time': 8}),
('partial repair probe: minimum before booking fee',
[{'meters': 0, 'seconds': 1374},
{'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 110, 'per_min': 15}],
{'distance': 0, 'fare': 875, 'time': 344}),
('second regression',
[{'meters': 500, 'seconds': 30},
{'base': 0, 'booking_fee': 175, 'minimum': 500, 'per_km': 110, 'per_min': 35}],
{'distance': 55, 'fare': 675, 'time': 18}),
('normal control 1',
[{'meters': 1004, 'seconds': 61},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 95, 'per_min': 15}],
{'distance': 95, 'fare': 500, 'time': 15}),
('normal control 2',
[{'meters': 0, 'seconds': 30},
{'base': 150, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 30}],
{'distance': 0, 'fare': 1000, 'time': 15}),
('normal control 3',
[{'meters': 12, 'seconds': 61},
{'base': 150, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 15}],
{'distance': 1, 'fare': 500, 'time': 15}),
('normal control 4',
[{'meters': 24766, 'seconds': 30},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 250, 'per_min': 30}],
{'distance': 6192, 'fare': 6707, 'time': 15})],
[('regression: minimum before booking fee',
[{'meters': 500, 'seconds': 61},
{'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 125, 'per_min': 35}],
{'distance': 63, 'fare': 750, 'time': 36}),
('partial repair probe: minimum before booking fee',
[{'meters': 500, 'seconds': 90},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 35}],
{'distance': 55, 'fare': 950, 'time': 53}),
('second regression',
[{'meters': 2500, 'seconds': 30},
{'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 15}],
{'distance': 625, 'fare': 875, 'time': 8}),
('normal control 1',
[{'meters': 4, 'seconds': 2383},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 1, 'fare': 1641, 'time': 1390}),
('normal control 2',
[{'meters': 0, 'seconds': 1117},
{'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 30}],
{'distance': 0, 'fare': 1059, 'time': 559}),
('normal control 3',
[{'meters': 0, 'seconds': 30}, {'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 35}],
{'distance': 0, 'fare': 700, 'time': 18}),
('normal control 4',
[{'meters': 12, 'seconds': 30},
{'base': 150, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 15}],
{'distance': 1, 'fare': 500, 'time': 8})]]
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: minimum before booking fee | {'distance': 0, 'fare': 1000, 'time': 18} | {'distance': 0, 'fare': 1250, 'time': 18} | Failed |
| partial repair probe: minimum before booking fee | {'distance': 0, 'fare': 500, 'time': 15} | {'distance': 0, 'fare': 750, 'time': 15} | Failed |
| second regression | {'distance': 0, 'fare': 700, 'time': 15} | {'distance': 0, 'fare': 875, 'time': 15} | Failed |
| normal control 1 | {'distance': 313, 'fare': 1000, 'time': 8} | {'distance': 313, 'fare': 1000, 'time': 8} | Passed |
| normal control 2 | {'distance': 0, 'fare': 700, 'time': 15} | {'distance': 0, 'fare': 700, 'time': 15} | Passed |
| normal control 3 | {'distance': 125, 'fare': 500, 'time': 0} | {'distance': 125, 'fare': 500, 'time': 0} | Passed |
| normal control 4 | {'distance': 0, 'fare': 500, 'time': 36} | {'distance': 0, 'fare': 500, 'time': 36} | Passed |
SHA-256 / bb8da4b96573f39d114270d36494014e1a3c2b0301e770f270d10a78bd6be3dc
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: minimum before booking fee',
[{'meters': 0, 'seconds': 30},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 125, 'per_min': 35}],
{'distance': 0, 'fare': 1250, 'time': 18}),
('partial repair probe: minimum before booking fee',
[{'meters': 4, 'seconds': 30},
{'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 95, 'per_min': 30}],
{'distance': 0, 'fare': 750, 'time': 15}),
('second regression',
[{'meters': 0, 'seconds': 30},
{'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 875, 'time': 15}),
('normal control 1',
[{'meters': 2500, 'seconds': 30},
{'base': 250, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 15}],
{'distance': 313, 'fare': 1000, 'time': 8}),
('normal control 2',
[{'meters': 0, 'seconds': 61},
{'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 125, 'per_min': 15}],
{'distance': 0, 'fare': 700, 'time': 15}),
('normal control 3',
[{'meters': 500, 'seconds': 0},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 250, 'per_min': 30}],
{'distance': 125, 'fare': 500, 'time': 0}),
('normal control 4',
[{'meters': 0, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 35}],
{'distance': 0, 'fare': 500, 'time': 36})],
[('regression: minimum before booking fee',
[{'meters': 2500, 'seconds': 90},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 15}],
{'distance': 313, 'fare': 950, 'time': 23}),
('partial repair probe: minimum before booking fee',
[{'meters': 0, 'seconds': 0},
{'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 95, 'per_min': 35}],
{'distance': 0, 'fare': 1175, 'time': 0}),
('second regression',
[{'meters': 0, 'seconds': 90},
{'base': 250, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 1175, 'time': 45}),
('normal control 1',
[{'meters': 15327, 'seconds': 30},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 30}],
{'distance': 1916, 'fare': 2181, 'time': 15}),
('normal control 2',
[{'meters': 500, 'seconds': 1882},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 250, 'per_min': 25}],
{'distance': 125, 'fare': 909, 'time': 784}),
('normal control 3',
[{'meters': 500, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 30}],
{'distance': 48, 'fare': 700, 'time': 31}),
('normal control 4',
[{'meters': 500, 'seconds': 0},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 35}],
{'distance': 48, 'fare': 700, 'time': 0})],
[('regression: minimum before booking fee',
[{'meters': 1004, 'seconds': 0},
{'base': 0, 'booking_fee': 175, 'minimum': 500, 'per_km': 250, 'per_min': 15}],
{'distance': 251, 'fare': 675, 'time': 0}),
('partial repair probe: minimum before booking fee',
[{'meters': 4, 'seconds': 0},
{'base': 150, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 30}],
{'distance': 0, 'fare': 950, 'time': 0}),
('second regression',
[{'meters': 12, 'seconds': 90},
{'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 30}],
{'distance': 1, 'fare': 950, 'time': 45}),
('normal control 1',
[{'meters': 0, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 15}],
{'distance': 0, 'fare': 500, 'time': 15}),
('normal control 2',
[{'meters': 6354, 'seconds': 30},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 35}],
{'distance': 699, 'fare': 717, 'time': 18}),
('normal control 3',
[{'meters': 4, 'seconds': 61},
{'base': 250, 'booking_fee': 0, 'minimum': 500, 'per_km': 95, 'per_min': 35}],
{'distance': 0, 'fare': 500, 'time': 36}),
('normal control 4',
[{'meters': 23665, 'seconds': 30},
{'base': 0, 'booking_fee': 175, 'minimum': 500, 'per_km': 110, 'per_min': 15}],
{'distance': 2603, 'fare': 2786, 'time': 8})],
[('regression: minimum before booking fee',
[{'meters': 500, 'seconds': 30},
{'base': 150, 'booking_fee': 175, 'minimum': 1000, 'per_km': 110, 'per_min': 15}],
{'distance': 55, 'fare': 1175, 'time': 8}),
('partial repair probe: minimum before booking fee',
[{'meters': 0, 'seconds': 1374},
{'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 110, 'per_min': 15}],
{'distance': 0, 'fare': 875, 'time': 344}),
('second regression',
[{'meters': 500, 'seconds': 30},
{'base': 0, 'booking_fee': 175, 'minimum': 500, 'per_km': 110, 'per_min': 35}],
{'distance': 55, 'fare': 675, 'time': 18}),
('normal control 1',
[{'meters': 1004, 'seconds': 61},
{'base': 0, 'booking_fee': 0, 'minimum': 500, 'per_km': 95, 'per_min': 15}],
{'distance': 95, 'fare': 500, 'time': 15}),
('normal control 2',
[{'meters': 0, 'seconds': 30},
{'base': 150, 'booking_fee': 0, 'minimum': 1000, 'per_km': 125, 'per_min': 30}],
{'distance': 0, 'fare': 1000, 'time': 15}),
('normal control 3',
[{'meters': 12, 'seconds': 61},
{'base': 150, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 15}],
{'distance': 1, 'fare': 500, 'time': 15}),
('normal control 4',
[{'meters': 24766, 'seconds': 30},
{'base': 250, 'booking_fee': 250, 'minimum': 1000, 'per_km': 250, 'per_min': 30}],
{'distance': 6192, 'fare': 6707, 'time': 15})],
[('regression: minimum before booking fee',
[{'meters': 500, 'seconds': 61},
{'base': 150, 'booking_fee': 250, 'minimum': 500, 'per_km': 125, 'per_min': 35}],
{'distance': 63, 'fare': 750, 'time': 36}),
('partial repair probe: minimum before booking fee',
[{'meters': 500, 'seconds': 90},
{'base': 0, 'booking_fee': 250, 'minimum': 700, 'per_km': 110, 'per_min': 35}],
{'distance': 55, 'fare': 950, 'time': 53}),
('second regression',
[{'meters': 2500, 'seconds': 30},
{'base': 0, 'booking_fee': 175, 'minimum': 700, 'per_km': 250, 'per_min': 15}],
{'distance': 625, 'fare': 875, 'time': 8}),
('normal control 1',
[{'meters': 4, 'seconds': 2383},
{'base': 250, 'booking_fee': 0, 'minimum': 700, 'per_km': 250, 'per_min': 35}],
{'distance': 1, 'fare': 1641, 'time': 1390}),
('normal control 2',
[{'meters': 0, 'seconds': 1117},
{'base': 250, 'booking_fee': 250, 'minimum': 700, 'per_km': 125, 'per_min': 30}],
{'distance': 0, 'fare': 1059, 'time': 559}),
('normal control 3',
[{'meters': 0, 'seconds': 30}, {'base': 0, 'booking_fee': 0, 'minimum': 700, 'per_km': 95, 'per_min': 35}],
{'distance': 0, 'fare': 700, 'time': 18}),
('normal control 4',
[{'meters': 12, 'seconds': 30},
{'base': 150, 'booking_fee': 0, 'minimum': 500, 'per_km': 110, 'per_min': 15}],
{'distance': 1, 'fare': 500, 'time': 8})]]
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: minimum before booking fee | {'distance': 0, 'fare': 1250, 'time': 18} | {'distance': 0, 'fare': 1250, 'time': 18} | Passed |
| partial repair probe: minimum before booking fee | {'distance': 0, 'fare': 750, 'time': 15} | {'distance': 0, 'fare': 750, 'time': 15} | Passed |
| second regression | {'distance': 0, 'fare': 875, 'time': 15} | {'distance': 0, 'fare': 875, 'time': 15} | Passed |
| normal control 1 | {'distance': 313, 'fare': 1000, 'time': 8} | {'distance': 313, 'fare': 1000, 'time': 8} | Passed |
| normal control 2 | {'distance': 0, 'fare': 700, 'time': 15} | {'distance': 0, 'fare': 700, 'time': 15} | Passed |
| normal control 3 | {'distance': 125, 'fare': 500, 'time': 0} | {'distance': 125, 'fare': 500, 'time': 0} | Passed |
| normal control 4 | {'distance': 0, 'fare': 500, 'time': 36} | {'distance': 0, 'fare': 500, 'time': 36} | Passed |
SHA-256 / dc517079c3795314231982fae97b64909f1a51998ac8a1a0b8336a8cc811e731
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 2026-09-29T14:50:39.582794+00:00.
Case digest / 46c866cc0aa02bc9468df282e2d73c480f7f2f04f5ba55395f71bebb1500d32c