FA-85586 / Ride-hailing fare and surge pricing / Open access
Promo rejected on its expiry day · case 01
Riders using a code on the last valid day are told it expired.
ROOT CAUSE
The expiry test uses >=.
VERIFIED REPAIR
Expire only after the expiry day.
Unsuccessful approach: Comparing seconds against a day index expires every code.
Case contract
Validate a promo in order: expired if now_day is after expires_day (the expiry day itself is valid), exhausted if no uses remain, not_first if first-ride-only and the rider has prior rides, min_fare if the fare (cents) is below min_fare. A valid pct promo takes value% of the fare (floored), capped at cap cents when cap is not None (a cap of 0 means a zero discount); a flat promo takes value but never more than the fare. Ride credits then cover up to the remaining fare. Return discount, credit used, amount due and reason.
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(promo, ride, now_day):
reason = None
if now_day >= promo['expires_day']:
reason = 'expired'
elif promo['uses_left'] <= 0:
reason = 'exhausted'
elif promo['first_ride_only'] and ride['rider_rides'] > 0:
reason = 'not_first'
elif ride['fare'] < promo['min_fare']:
reason = 'min_fare'
disc = 0
if reason is None:
if promo['kind'] == 'pct':
disc = ride['fare'] * promo['value'] // 100
if promo['cap'] is not None:
disc = min(disc, promo['cap'])
else:
disc = min(promo['value'], ride['fare'])
credit = min(ride['credits'], ride['fare'] - disc)
return {'discount': disc, 'credit_used': credit, 'due': ride['fare'] - disc - credit, 'reason': reason}
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: expiry day inclusive',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 1000},
{'credits': 5000, 'fare': 1050, 'rider_rides': 0}, 100],
{'credit_used': 50, 'discount': 1000, 'due': 0, 'reason': None}),
('partial repair probe: expiry day inclusive',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 0,
'value': 2500},
{'credits': 0, 'fare': 1050, 'rider_rides': 1}, 100],
{'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'exhausted'}),
('second regression',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 500},
{'credits': 5000, 'fare': 3315, 'rider_rides': 1}, 100],
{'credit_used': 3315, 'discount': 0, 'due': 0, 'reason': 'not_first'}),
('normal control 1',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 0,
'value': 25},
{'credits': 0, 'fare': 3279, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 3279, 'reason': 'expired'}),
('normal control 2',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 3,
'value': 1000},
{'credits': 5000, 'fare': 700, 'rider_rides': 5}, 101],
{'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 3',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 0,
'value': 1000},
{'credits': 5000, 'fare': 700, 'rider_rides': 1}, 101],
{'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 4',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 500},
{'credits': 5000, 'fare': 2400, 'rider_rides': 1}, 101],
{'credit_used': 2400, 'discount': 0, 'due': 0, 'reason': 'expired'})],
[('regression: expiry day inclusive',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 2500},
{'credits': 250, 'fare': 1000, 'rider_rides': 5}, 100],
{'credit_used': 250, 'discount': 0, 'due': 750, 'reason': 'exhausted'}),
('partial repair probe: expiry day inclusive',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 0,
'value': 2500},
{'credits': 250, 'fare': 1099, 'rider_rides': 0}, 100],
{'credit_used': 250, 'discount': 0, 'due': 849, 'reason': 'exhausted'}),
('second regression',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 0,
'value': 25},
{'credits': 0, 'fare': 1000, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'exhausted'}),
('normal control 1',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 3,
'value': 2500},
{'credits': 0, 'fare': 1000, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'expired'}),
('normal control 2',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 1,
'value': 1000},
{'credits': 0, 'fare': 1000, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'expired'}),
('normal control 3',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 1000},
{'credits': 0, 'fare': 1099, 'rider_rides': 1}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1099, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 1000},
{'credits': 5000, 'fare': 3865, 'rider_rides': 5}, 101],
{'credit_used': 3865, 'discount': 0, 'due': 0, 'reason': 'expired'})],
[('regression: expiry day inclusive',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 2500},
{'credits': 5000, 'fare': 3055, 'rider_rides': 0}, 100],
{'credit_used': 555, 'discount': 2500, 'due': 0, 'reason': None}),
('partial repair probe: expiry day inclusive',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 2500},
{'credits': 0, 'fare': 1099, 'rider_rides': 1}, 99],
{'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None}),
('second regression',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 0,
'value': 10},
{'credits': 0, 'fare': 700, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'exhausted'}),
('normal control 1',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 10},
{'credits': 250, 'fare': 2400, 'rider_rides': 5}, 101],
{'credit_used': 250, 'discount': 0, 'due': 2150, 'reason': 'expired'}),
('normal control 2',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 500},
{'credits': 0, 'fare': 1000, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'expired'}),
('normal control 3',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 10},
{'credits': 5000, 'fare': 1050, 'rider_rides': 1}, 101],
{'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 2500},
{'credits': 5000, 'fare': 1000, 'rider_rides': 1}, 101],
{'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'expired'})],
[('regression: expiry day inclusive',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 2500},
{'credits': 0, 'fare': 2400, 'rider_rides': 5}, 100],
{'credit_used': 0, 'discount': 2400, 'due': 0, 'reason': None}),
('partial repair probe: expiry day inclusive',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 0,
'value': 25},
{'credits': 0, 'fare': 700, 'rider_rides': 0}, 99],
{'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'exhausted'}),
('second regression',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 1,
'value': 500},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 500, 'due': 1900, 'reason': None}),
('normal control 1',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 1099, 'rider_rides': 0}, 101],
{'credit_used': 1099, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 2',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 500},
{'credits': 5000, 'fare': 1318, 'rider_rides': 0}, 101],
{'credit_used': 1318, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 3',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 0,
'value': 10},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 2400, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 3,
'value': 10},
{'credits': 0, 'fare': 2400, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 2400, 'reason': 'expired'})],
[('regression: expiry day inclusive',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 1050, 'rider_rides': 1}, 100],
{'credit_used': 525, 'discount': 525, 'due': 0, 'reason': None}),
('partial repair probe: expiry day inclusive',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 2400, 'rider_rides': 1}, 99],
{'credit_used': 1200, 'discount': 1200, 'due': 0, 'reason': None}),
('second regression',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 10},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 240, 'due': 2160, 'reason': None}),
('normal control 1',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 500},
{'credits': 5000, 'fare': 1000, 'rider_rides': 1}, 101],
{'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 2',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 2500},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 2400, 'reason': 'expired'}),
('normal control 3',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 1,
'value': 10},
{'credits': 0, 'fare': 1000, 'rider_rides': 1}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 1000},
{'credits': 5000, 'fare': 1050, 'rider_rides': 5}, 101],
{'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'expired'})]]
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: expiry day inclusive | {'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 50, 'discount': 1000, 'due': 0, 'reason': None} | Failed |
| partial repair probe: expiry day inclusive | {'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'} | {'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'exhausted'} | Failed |
| second regression | {'credit_used': 3315, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 3315, 'discount': 0, 'due': 0, 'reason': 'not_first'} | Failed |
| normal control 1 | {'credit_used': 0, 'discount': 0, 'due': 3279, 'reason': 'expired'} | {'credit_used': 0, 'discount': 0, 'due': 3279, 'reason': 'expired'} | Passed |
| normal control 2 | {'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'} | Passed |
| normal control 3 | {'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'} | Passed |
| normal control 4 | {'credit_used': 2400, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 2400, 'discount': 0, 'due': 0, 'reason': 'expired'} | Passed |
SHA-256 / b53ec76200170cfe612a3457664e8d0af53416d3777bfa4138801cb7df0264fd
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(promo, ride, now_day):
reason = None
if now_day * 86400 > promo['expires_day']:
reason = 'expired'
elif promo['uses_left'] <= 0:
reason = 'exhausted'
elif promo['first_ride_only'] and ride['rider_rides'] > 0:
reason = 'not_first'
elif ride['fare'] < promo['min_fare']:
reason = 'min_fare'
disc = 0
if reason is None:
if promo['kind'] == 'pct':
disc = ride['fare'] * promo['value'] // 100
if promo['cap'] is not None:
disc = min(disc, promo['cap'])
else:
disc = min(promo['value'], ride['fare'])
credit = min(ride['credits'], ride['fare'] - disc)
return {'discount': disc, 'credit_used': credit, 'due': ride['fare'] - disc - credit, 'reason': reason}
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: expiry day inclusive',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 1000},
{'credits': 5000, 'fare': 1050, 'rider_rides': 0}, 100],
{'credit_used': 50, 'discount': 1000, 'due': 0, 'reason': None}),
('partial repair probe: expiry day inclusive',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 0,
'value': 2500},
{'credits': 0, 'fare': 1050, 'rider_rides': 1}, 100],
{'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'exhausted'}),
('second regression',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 500},
{'credits': 5000, 'fare': 3315, 'rider_rides': 1}, 100],
{'credit_used': 3315, 'discount': 0, 'due': 0, 'reason': 'not_first'}),
('normal control 1',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 0,
'value': 25},
{'credits': 0, 'fare': 3279, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 3279, 'reason': 'expired'}),
('normal control 2',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 3,
'value': 1000},
{'credits': 5000, 'fare': 700, 'rider_rides': 5}, 101],
{'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 3',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 0,
'value': 1000},
{'credits': 5000, 'fare': 700, 'rider_rides': 1}, 101],
{'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 4',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 500},
{'credits': 5000, 'fare': 2400, 'rider_rides': 1}, 101],
{'credit_used': 2400, 'discount': 0, 'due': 0, 'reason': 'expired'})],
[('regression: expiry day inclusive',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 2500},
{'credits': 250, 'fare': 1000, 'rider_rides': 5}, 100],
{'credit_used': 250, 'discount': 0, 'due': 750, 'reason': 'exhausted'}),
('partial repair probe: expiry day inclusive',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 0,
'value': 2500},
{'credits': 250, 'fare': 1099, 'rider_rides': 0}, 100],
{'credit_used': 250, 'discount': 0, 'due': 849, 'reason': 'exhausted'}),
('second regression',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 0,
'value': 25},
{'credits': 0, 'fare': 1000, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'exhausted'}),
('normal control 1',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 3,
'value': 2500},
{'credits': 0, 'fare': 1000, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'expired'}),
('normal control 2',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 1,
'value': 1000},
{'credits': 0, 'fare': 1000, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'expired'}),
('normal control 3',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 1000},
{'credits': 0, 'fare': 1099, 'rider_rides': 1}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1099, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 1000},
{'credits': 5000, 'fare': 3865, 'rider_rides': 5}, 101],
{'credit_used': 3865, 'discount': 0, 'due': 0, 'reason': 'expired'})],
[('regression: expiry day inclusive',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 2500},
{'credits': 5000, 'fare': 3055, 'rider_rides': 0}, 100],
{'credit_used': 555, 'discount': 2500, 'due': 0, 'reason': None}),
('partial repair probe: expiry day inclusive',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 2500},
{'credits': 0, 'fare': 1099, 'rider_rides': 1}, 99],
{'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None}),
('second regression',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 0,
'value': 10},
{'credits': 0, 'fare': 700, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'exhausted'}),
('normal control 1',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 10},
{'credits': 250, 'fare': 2400, 'rider_rides': 5}, 101],
{'credit_used': 250, 'discount': 0, 'due': 2150, 'reason': 'expired'}),
('normal control 2',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 500},
{'credits': 0, 'fare': 1000, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'expired'}),
('normal control 3',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 10},
{'credits': 5000, 'fare': 1050, 'rider_rides': 1}, 101],
{'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 2500},
{'credits': 5000, 'fare': 1000, 'rider_rides': 1}, 101],
{'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'expired'})],
[('regression: expiry day inclusive',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 2500},
{'credits': 0, 'fare': 2400, 'rider_rides': 5}, 100],
{'credit_used': 0, 'discount': 2400, 'due': 0, 'reason': None}),
('partial repair probe: expiry day inclusive',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 0,
'value': 25},
{'credits': 0, 'fare': 700, 'rider_rides': 0}, 99],
{'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'exhausted'}),
('second regression',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 1,
'value': 500},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 500, 'due': 1900, 'reason': None}),
('normal control 1',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 1099, 'rider_rides': 0}, 101],
{'credit_used': 1099, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 2',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 500},
{'credits': 5000, 'fare': 1318, 'rider_rides': 0}, 101],
{'credit_used': 1318, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 3',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 0,
'value': 10},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 2400, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 3,
'value': 10},
{'credits': 0, 'fare': 2400, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 2400, 'reason': 'expired'})],
[('regression: expiry day inclusive',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 1050, 'rider_rides': 1}, 100],
{'credit_used': 525, 'discount': 525, 'due': 0, 'reason': None}),
('partial repair probe: expiry day inclusive',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 2400, 'rider_rides': 1}, 99],
{'credit_used': 1200, 'discount': 1200, 'due': 0, 'reason': None}),
('second regression',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 10},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 240, 'due': 2160, 'reason': None}),
('normal control 1',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 500},
{'credits': 5000, 'fare': 1000, 'rider_rides': 1}, 101],
{'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 2',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 2500},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 2400, 'reason': 'expired'}),
('normal control 3',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 1,
'value': 10},
{'credits': 0, 'fare': 1000, 'rider_rides': 1}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 1000},
{'credits': 5000, 'fare': 1050, 'rider_rides': 5}, 101],
{'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'expired'})]]
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: expiry day inclusive | {'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 50, 'discount': 1000, 'due': 0, 'reason': None} | Failed |
| partial repair probe: expiry day inclusive | {'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'} | {'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'exhausted'} | Failed |
| second regression | {'credit_used': 3315, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 3315, 'discount': 0, 'due': 0, 'reason': 'not_first'} | Failed |
| normal control 1 | {'credit_used': 0, 'discount': 0, 'due': 3279, 'reason': 'expired'} | {'credit_used': 0, 'discount': 0, 'due': 3279, 'reason': 'expired'} | Passed |
| normal control 2 | {'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'} | Passed |
| normal control 3 | {'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'} | Passed |
| normal control 4 | {'credit_used': 2400, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 2400, 'discount': 0, 'due': 0, 'reason': 'expired'} | Passed |
SHA-256 / 1849b8b2694009985860e3acb6199cd799c2043a7977288954ec8867b05fe0c3
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(promo, ride, now_day):
reason = None
if now_day > promo['expires_day']:
reason = 'expired'
elif promo['uses_left'] <= 0:
reason = 'exhausted'
elif promo['first_ride_only'] and ride['rider_rides'] > 0:
reason = 'not_first'
elif ride['fare'] < promo['min_fare']:
reason = 'min_fare'
disc = 0
if reason is None:
if promo['kind'] == 'pct':
disc = ride['fare'] * promo['value'] // 100
if promo['cap'] is not None:
disc = min(disc, promo['cap'])
else:
disc = min(promo['value'], ride['fare'])
credit = min(ride['credits'], ride['fare'] - disc)
return {'discount': disc, 'credit_used': credit, 'due': ride['fare'] - disc - credit, 'reason': reason}
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression: expiry day inclusive',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 1000},
{'credits': 5000, 'fare': 1050, 'rider_rides': 0}, 100],
{'credit_used': 50, 'discount': 1000, 'due': 0, 'reason': None}),
('partial repair probe: expiry day inclusive',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 0,
'value': 2500},
{'credits': 0, 'fare': 1050, 'rider_rides': 1}, 100],
{'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'exhausted'}),
('second regression',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 500},
{'credits': 5000, 'fare': 3315, 'rider_rides': 1}, 100],
{'credit_used': 3315, 'discount': 0, 'due': 0, 'reason': 'not_first'}),
('normal control 1',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 0,
'value': 25},
{'credits': 0, 'fare': 3279, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 3279, 'reason': 'expired'}),
('normal control 2',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 3,
'value': 1000},
{'credits': 5000, 'fare': 700, 'rider_rides': 5}, 101],
{'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 3',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 0,
'value': 1000},
{'credits': 5000, 'fare': 700, 'rider_rides': 1}, 101],
{'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 4',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 500},
{'credits': 5000, 'fare': 2400, 'rider_rides': 1}, 101],
{'credit_used': 2400, 'discount': 0, 'due': 0, 'reason': 'expired'})],
[('regression: expiry day inclusive',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 2500},
{'credits': 250, 'fare': 1000, 'rider_rides': 5}, 100],
{'credit_used': 250, 'discount': 0, 'due': 750, 'reason': 'exhausted'}),
('partial repair probe: expiry day inclusive',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 0,
'value': 2500},
{'credits': 250, 'fare': 1099, 'rider_rides': 0}, 100],
{'credit_used': 250, 'discount': 0, 'due': 849, 'reason': 'exhausted'}),
('second regression',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 0,
'value': 25},
{'credits': 0, 'fare': 1000, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'exhausted'}),
('normal control 1',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 3,
'value': 2500},
{'credits': 0, 'fare': 1000, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'expired'}),
('normal control 2',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 1,
'value': 1000},
{'credits': 0, 'fare': 1000, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'expired'}),
('normal control 3',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 1000},
{'credits': 0, 'fare': 1099, 'rider_rides': 1}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1099, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 1000},
{'credits': 5000, 'fare': 3865, 'rider_rides': 5}, 101],
{'credit_used': 3865, 'discount': 0, 'due': 0, 'reason': 'expired'})],
[('regression: expiry day inclusive',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 2500},
{'credits': 5000, 'fare': 3055, 'rider_rides': 0}, 100],
{'credit_used': 555, 'discount': 2500, 'due': 0, 'reason': None}),
('partial repair probe: expiry day inclusive',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 2500},
{'credits': 0, 'fare': 1099, 'rider_rides': 1}, 99],
{'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None}),
('second regression',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 0,
'value': 10},
{'credits': 0, 'fare': 700, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'exhausted'}),
('normal control 1',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 10},
{'credits': 250, 'fare': 2400, 'rider_rides': 5}, 101],
{'credit_used': 250, 'discount': 0, 'due': 2150, 'reason': 'expired'}),
('normal control 2',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 500},
{'credits': 0, 'fare': 1000, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'expired'}),
('normal control 3',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 10},
{'credits': 5000, 'fare': 1050, 'rider_rides': 1}, 101],
{'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 2500},
{'credits': 5000, 'fare': 1000, 'rider_rides': 1}, 101],
{'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'expired'})],
[('regression: expiry day inclusive',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 2500},
{'credits': 0, 'fare': 2400, 'rider_rides': 5}, 100],
{'credit_used': 0, 'discount': 2400, 'due': 0, 'reason': None}),
('partial repair probe: expiry day inclusive',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 0,
'value': 25},
{'credits': 0, 'fare': 700, 'rider_rides': 0}, 99],
{'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'exhausted'}),
('second regression',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 1,
'value': 500},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 500, 'due': 1900, 'reason': None}),
('normal control 1',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 1099, 'rider_rides': 0}, 101],
{'credit_used': 1099, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 2',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 500},
{'credits': 5000, 'fare': 1318, 'rider_rides': 0}, 101],
{'credit_used': 1318, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 3',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 0,
'value': 10},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 2400, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 3,
'value': 10},
{'credits': 0, 'fare': 2400, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 2400, 'reason': 'expired'})],
[('regression: expiry day inclusive',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 1050, 'rider_rides': 1}, 100],
{'credit_used': 525, 'discount': 525, 'due': 0, 'reason': None}),
('partial repair probe: expiry day inclusive',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 2400, 'rider_rides': 1}, 99],
{'credit_used': 1200, 'discount': 1200, 'due': 0, 'reason': None}),
('second regression',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 10},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 240, 'due': 2160, 'reason': None}),
('normal control 1',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 500},
{'credits': 5000, 'fare': 1000, 'rider_rides': 1}, 101],
{'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 2',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 2500},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 2400, 'reason': 'expired'}),
('normal control 3',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 1,
'value': 10},
{'credits': 0, 'fare': 1000, 'rider_rides': 1}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 1000},
{'credits': 5000, 'fare': 1050, 'rider_rides': 5}, 101],
{'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'expired'})]]
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: expiry day inclusive | {'credit_used': 50, 'discount': 1000, 'due': 0, 'reason': None} | {'credit_used': 50, 'discount': 1000, 'due': 0, 'reason': None} | Passed |
| partial repair probe: expiry day inclusive | {'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'exhausted'} | {'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'exhausted'} | Passed |
| second regression | {'credit_used': 3315, 'discount': 0, 'due': 0, 'reason': 'not_first'} | {'credit_used': 3315, 'discount': 0, 'due': 0, 'reason': 'not_first'} | Passed |
| normal control 1 | {'credit_used': 0, 'discount': 0, 'due': 3279, 'reason': 'expired'} | {'credit_used': 0, 'discount': 0, 'due': 3279, 'reason': 'expired'} | Passed |
| normal control 2 | {'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'} | Passed |
| normal control 3 | {'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 700, 'discount': 0, 'due': 0, 'reason': 'expired'} | Passed |
| normal control 4 | {'credit_used': 2400, 'discount': 0, 'due': 0, 'reason': 'expired'} | {'credit_used': 2400, 'discount': 0, 'due': 0, 'reason': 'expired'} | Passed |
SHA-256 / 5cc6df6324ff758315c8f08d7200c61e7caa79b41ef24ad1546351f336a535dc
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 2026-09-29T14:50:41.791732+00:00.
Case digest / 915dd5e145d9c2147e62d98d8395eba29b4c534fd8a10ed828818ec164fce84c