FA-85601 / Ride-hailing fare and surge pricing / Open access
Ride credits consumed on the pre-discount fare · case 01
A rider with credits and a promo is charged negative amounts and loses credit balance.
ROOT CAUSE
Credit usage is limited by the full fare instead of the fare after discount.
VERIFIED REPAIR
Apply credits to the post-discount remainder.
Unsuccessful approach: Refusing to combine credits with a promo leaves the rider paying cash they had credit for.
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'])
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: credits after discount',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 10},
{'credits': 5000, 'fare': 2400, 'rider_rides': 1}, 100],
{'credit_used': 2160, 'discount': 240, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 3459, 'rider_rides': 1}, 100],
{'credit_used': 1730, 'discount': 1729, 'due': 0, 'reason': None}),
('second regression',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 2500},
{'credits': 5000, 'fare': 1099, 'rider_rides': 0}, 99],
{'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 0,
'value': 25},
{'credits': 5000, 'fare': 1000, 'rider_rides': 5}, 100],
{'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'exhausted'}),
('normal control 2',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 10},
{'credits': 250, 'fare': 700, 'rider_rides': 5}, 99],
{'credit_used': 250, 'discount': 0, 'due': 450, 'reason': 'min_fare'}),
('normal control 3',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 2500},
{'credits': 0, 'fare': 700, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 0,
'value': 50},
{'credits': 0, 'fare': 1050, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'})],
[('regression: credits after discount',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 3646, 'rider_rides': 5}, 100],
{'credit_used': 1823, 'discount': 1823, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 2500},
{'credits': 250, 'fare': 3192, 'rider_rides': 0}, 100],
{'credit_used': 250, 'discount': 2500, 'due': 442, 'reason': None}),
('second regression',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 1099, 'rider_rides': 0}, 99],
{'credit_used': 799, 'discount': 300, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 3,
'value': 1000},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 1000, 'due': 1400, 'reason': None}),
('normal control 2',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 3,
'value': 2500},
{'credits': 5000, 'fare': 1099, 'rider_rides': 5}, 99],
{'credit_used': 1099, 'discount': 0, 'due': 0, 'reason': 'not_first'}),
('normal control 3',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 25},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 99],
{'credit_used': 0, 'discount': 600, 'due': 1800, 'reason': None}),
('normal control 4',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 500},
{'credits': 0, 'fare': 632, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 632, 'reason': 'expired'})],
[('regression: credits after discount',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 2500},
{'credits': 250, 'fare': 1099, 'rider_rides': 1}, 100],
{'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 10},
{'credits': 250, 'fare': 2836, 'rider_rides': 0}, 99],
{'credit_used': 250, 'discount': 283, 'due': 2303, 'reason': None}),
('second regression',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 3,
'value': 1000},
{'credits': 5000, 'fare': 1099, 'rider_rides': 1}, 100],
{'credit_used': 99, 'discount': 1000, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 10},
{'credits': 0, 'fare': 1099, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 109, 'due': 990, 'reason': None}),
('normal control 2',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 500},
{'credits': 5000, 'fare': 1050, 'rider_rides': 5}, 100],
{'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'exhausted'}),
('normal control 3',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 50},
{'credits': 5000, 'fare': 1275, 'rider_rides': 0}, 101],
{'credit_used': 1275, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 4',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 3,
'value': 500},
{'credits': 0, 'fare': 1050, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 500, 'due': 550, 'reason': None})],
[('regression: credits after discount',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 2500},
{'credits': 5000, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 2400, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 3,
'value': 500},
{'credits': 5000, 'fare': 2400, 'rider_rides': 0}, 99],
{'credit_used': 1900, 'discount': 500, 'due': 0, 'reason': None}),
('second regression',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 2500},
{'credits': 250, 'fare': 1000, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 1000, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 0,
'uses_left': 3,
'value': 25},
{'credits': 0, 'fare': 1050, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 262, 'due': 788, 'reason': None}),
('normal control 2',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 0,
'value': 50},
{'credits': 5000, 'fare': 1000, 'rider_rides': 1}, 100],
{'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'exhausted'}),
('normal control 3',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 2500},
{'credits': 0, 'fare': 700, 'rider_rides': 1}, 99],
{'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'not_first'}),
('normal control 4',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 0,
'value': 2500},
{'credits': 5000, 'fare': 1050, 'rider_rides': 1}, 100],
{'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'exhausted'})],
[('regression: credits after discount',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 1,
'value': 25},
{'credits': 5000, 'fare': 1050, 'rider_rides': 1}, 99],
{'credit_used': 788, 'discount': 262, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 25},
{'credits': 250, 'fare': 3234, 'rider_rides': 0}, 99],
{'credit_used': 250, 'discount': 808, 'due': 2176, 'reason': None}),
('second regression',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 25},
{'credits': 5000, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 1800, 'discount': 600, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 3,
'value': 500},
{'credits': 0, 'fare': 1050, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'}),
('normal control 2',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 0,
'value': 50},
{'credits': 0, 'fare': 1000, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'exhausted'}),
('normal control 3',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 10},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 240, 'due': 2160, 'reason': None}),
('normal control 4',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 1,
'value': 1000},
{'credits': 0, 'fare': 2400, 'rider_rides': 5}, 99],
{'credit_used': 0, 'discount': 0, 'due': 2400, 'reason': 'not_first'})]]
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: credits after discount | {'credit_used': 2400, 'discount': 240, 'due': -240, 'reason': None} | {'credit_used': 2160, 'discount': 240, 'due': 0, 'reason': None} | Failed |
| partial repair probe: credits after discount | {'credit_used': 3459, 'discount': 1729, 'due': -1729, 'reason': None} | {'credit_used': 1730, 'discount': 1729, 'due': 0, 'reason': None} | Failed |
| second regression | {'credit_used': 1099, 'discount': 1099, 'due': -1099, 'reason': None} | {'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None} | Failed |
| normal control 1 | {'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'exhausted'} | {'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'exhausted'} | Passed |
| normal control 2 | {'credit_used': 250, 'discount': 0, 'due': 450, 'reason': 'min_fare'} | {'credit_used': 250, 'discount': 0, 'due': 450, 'reason': 'min_fare'} | Passed |
| normal control 3 | {'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'expired'} | {'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'expired'} | Passed |
| normal control 4 | {'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'} | {'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'} | Passed |
SHA-256 / bdbe92079f11fbce63138388c6973e57c1428053bb4c57e4d23d8a48bff4fdb6
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 > 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) if disc == 0 else 0
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: credits after discount',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 10},
{'credits': 5000, 'fare': 2400, 'rider_rides': 1}, 100],
{'credit_used': 2160, 'discount': 240, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 3459, 'rider_rides': 1}, 100],
{'credit_used': 1730, 'discount': 1729, 'due': 0, 'reason': None}),
('second regression',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 2500},
{'credits': 5000, 'fare': 1099, 'rider_rides': 0}, 99],
{'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 0,
'value': 25},
{'credits': 5000, 'fare': 1000, 'rider_rides': 5}, 100],
{'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'exhausted'}),
('normal control 2',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 10},
{'credits': 250, 'fare': 700, 'rider_rides': 5}, 99],
{'credit_used': 250, 'discount': 0, 'due': 450, 'reason': 'min_fare'}),
('normal control 3',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 2500},
{'credits': 0, 'fare': 700, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 0,
'value': 50},
{'credits': 0, 'fare': 1050, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'})],
[('regression: credits after discount',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 3646, 'rider_rides': 5}, 100],
{'credit_used': 1823, 'discount': 1823, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 2500},
{'credits': 250, 'fare': 3192, 'rider_rides': 0}, 100],
{'credit_used': 250, 'discount': 2500, 'due': 442, 'reason': None}),
('second regression',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 1099, 'rider_rides': 0}, 99],
{'credit_used': 799, 'discount': 300, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 3,
'value': 1000},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 1000, 'due': 1400, 'reason': None}),
('normal control 2',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 3,
'value': 2500},
{'credits': 5000, 'fare': 1099, 'rider_rides': 5}, 99],
{'credit_used': 1099, 'discount': 0, 'due': 0, 'reason': 'not_first'}),
('normal control 3',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 25},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 99],
{'credit_used': 0, 'discount': 600, 'due': 1800, 'reason': None}),
('normal control 4',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 500},
{'credits': 0, 'fare': 632, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 632, 'reason': 'expired'})],
[('regression: credits after discount',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 2500},
{'credits': 250, 'fare': 1099, 'rider_rides': 1}, 100],
{'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 10},
{'credits': 250, 'fare': 2836, 'rider_rides': 0}, 99],
{'credit_used': 250, 'discount': 283, 'due': 2303, 'reason': None}),
('second regression',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 3,
'value': 1000},
{'credits': 5000, 'fare': 1099, 'rider_rides': 1}, 100],
{'credit_used': 99, 'discount': 1000, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 10},
{'credits': 0, 'fare': 1099, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 109, 'due': 990, 'reason': None}),
('normal control 2',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 500},
{'credits': 5000, 'fare': 1050, 'rider_rides': 5}, 100],
{'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'exhausted'}),
('normal control 3',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 50},
{'credits': 5000, 'fare': 1275, 'rider_rides': 0}, 101],
{'credit_used': 1275, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 4',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 3,
'value': 500},
{'credits': 0, 'fare': 1050, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 500, 'due': 550, 'reason': None})],
[('regression: credits after discount',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 2500},
{'credits': 5000, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 2400, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 3,
'value': 500},
{'credits': 5000, 'fare': 2400, 'rider_rides': 0}, 99],
{'credit_used': 1900, 'discount': 500, 'due': 0, 'reason': None}),
('second regression',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 2500},
{'credits': 250, 'fare': 1000, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 1000, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 0,
'uses_left': 3,
'value': 25},
{'credits': 0, 'fare': 1050, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 262, 'due': 788, 'reason': None}),
('normal control 2',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 0,
'value': 50},
{'credits': 5000, 'fare': 1000, 'rider_rides': 1}, 100],
{'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'exhausted'}),
('normal control 3',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 2500},
{'credits': 0, 'fare': 700, 'rider_rides': 1}, 99],
{'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'not_first'}),
('normal control 4',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 0,
'value': 2500},
{'credits': 5000, 'fare': 1050, 'rider_rides': 1}, 100],
{'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'exhausted'})],
[('regression: credits after discount',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 1,
'value': 25},
{'credits': 5000, 'fare': 1050, 'rider_rides': 1}, 99],
{'credit_used': 788, 'discount': 262, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 25},
{'credits': 250, 'fare': 3234, 'rider_rides': 0}, 99],
{'credit_used': 250, 'discount': 808, 'due': 2176, 'reason': None}),
('second regression',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 25},
{'credits': 5000, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 1800, 'discount': 600, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 3,
'value': 500},
{'credits': 0, 'fare': 1050, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'}),
('normal control 2',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 0,
'value': 50},
{'credits': 0, 'fare': 1000, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'exhausted'}),
('normal control 3',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 10},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 240, 'due': 2160, 'reason': None}),
('normal control 4',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 1,
'value': 1000},
{'credits': 0, 'fare': 2400, 'rider_rides': 5}, 99],
{'credit_used': 0, 'discount': 0, 'due': 2400, 'reason': 'not_first'})]]
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: credits after discount | {'credit_used': 0, 'discount': 240, 'due': 2160, 'reason': None} | {'credit_used': 2160, 'discount': 240, 'due': 0, 'reason': None} | Failed |
| partial repair probe: credits after discount | {'credit_used': 0, 'discount': 1729, 'due': 1730, 'reason': None} | {'credit_used': 1730, 'discount': 1729, 'due': 0, 'reason': None} | Failed |
| second regression | {'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None} | {'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None} | Passed |
| normal control 1 | {'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'exhausted'} | {'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'exhausted'} | Passed |
| normal control 2 | {'credit_used': 250, 'discount': 0, 'due': 450, 'reason': 'min_fare'} | {'credit_used': 250, 'discount': 0, 'due': 450, 'reason': 'min_fare'} | Passed |
| normal control 3 | {'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'expired'} | {'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'expired'} | Passed |
| normal control 4 | {'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'} | {'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'} | Passed |
SHA-256 / 9de0de8bb8b55684dfed19cd12722b1406d1f7409429641e984cd67cc37a89a0
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: credits after discount',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 10},
{'credits': 5000, 'fare': 2400, 'rider_rides': 1}, 100],
{'credit_used': 2160, 'discount': 240, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 3459, 'rider_rides': 1}, 100],
{'credit_used': 1730, 'discount': 1729, 'due': 0, 'reason': None}),
('second regression',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 2500},
{'credits': 5000, 'fare': 1099, 'rider_rides': 0}, 99],
{'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 0,
'value': 25},
{'credits': 5000, 'fare': 1000, 'rider_rides': 5}, 100],
{'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'exhausted'}),
('normal control 2',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 10},
{'credits': 250, 'fare': 700, 'rider_rides': 5}, 99],
{'credit_used': 250, 'discount': 0, 'due': 450, 'reason': 'min_fare'}),
('normal control 3',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 2500},
{'credits': 0, 'fare': 700, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'expired'}),
('normal control 4',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 0,
'value': 50},
{'credits': 0, 'fare': 1050, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'})],
[('regression: credits after discount',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 3646, 'rider_rides': 5}, 100],
{'credit_used': 1823, 'discount': 1823, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 2500},
{'credits': 250, 'fare': 3192, 'rider_rides': 0}, 100],
{'credit_used': 250, 'discount': 2500, 'due': 442, 'reason': None}),
('second regression',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 50},
{'credits': 5000, 'fare': 1099, 'rider_rides': 0}, 99],
{'credit_used': 799, 'discount': 300, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 3,
'value': 1000},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 1000, 'due': 1400, 'reason': None}),
('normal control 2',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 3,
'value': 2500},
{'credits': 5000, 'fare': 1099, 'rider_rides': 5}, 99],
{'credit_used': 1099, 'discount': 0, 'due': 0, 'reason': 'not_first'}),
('normal control 3',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 25},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 99],
{'credit_used': 0, 'discount': 600, 'due': 1800, 'reason': None}),
('normal control 4',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 500},
{'credits': 0, 'fare': 632, 'rider_rides': 5}, 101],
{'credit_used': 0, 'discount': 0, 'due': 632, 'reason': 'expired'})],
[('regression: credits after discount',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 1,
'value': 2500},
{'credits': 250, 'fare': 1099, 'rider_rides': 1}, 100],
{'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 10},
{'credits': 250, 'fare': 2836, 'rider_rides': 0}, 99],
{'credit_used': 250, 'discount': 283, 'due': 2303, 'reason': None}),
('second regression',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 3,
'value': 1000},
{'credits': 5000, 'fare': 1099, 'rider_rides': 1}, 100],
{'credit_used': 99, 'discount': 1000, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 10},
{'credits': 0, 'fare': 1099, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 109, 'due': 990, 'reason': None}),
('normal control 2',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 0,
'value': 500},
{'credits': 5000, 'fare': 1050, 'rider_rides': 5}, 100],
{'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'exhausted'}),
('normal control 3',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 3,
'value': 50},
{'credits': 5000, 'fare': 1275, 'rider_rides': 0}, 101],
{'credit_used': 1275, 'discount': 0, 'due': 0, 'reason': 'expired'}),
('normal control 4',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 3,
'value': 500},
{'credits': 0, 'fare': 1050, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 500, 'due': 550, 'reason': None})],
[('regression: credits after discount',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 2500},
{'credits': 5000, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 2400, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 3,
'value': 500},
{'credits': 5000, 'fare': 2400, 'rider_rides': 0}, 99],
{'credit_used': 1900, 'discount': 500, 'due': 0, 'reason': None}),
('second regression',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 3,
'value': 2500},
{'credits': 250, 'fare': 1000, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 1000, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 0,
'uses_left': 3,
'value': 25},
{'credits': 0, 'fare': 1050, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 262, 'due': 788, 'reason': None}),
('normal control 2',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 0,
'value': 50},
{'credits': 5000, 'fare': 1000, 'rider_rides': 1}, 100],
{'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'exhausted'}),
('normal control 3',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 0,
'uses_left': 1,
'value': 2500},
{'credits': 0, 'fare': 700, 'rider_rides': 1}, 99],
{'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'not_first'}),
('normal control 4',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1050,
'uses_left': 0,
'value': 2500},
{'credits': 5000, 'fare': 1050, 'rider_rides': 1}, 100],
{'credit_used': 1050, 'discount': 0, 'due': 0, 'reason': 'exhausted'})],
[('regression: credits after discount',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 1,
'value': 25},
{'credits': 5000, 'fare': 1050, 'rider_rides': 1}, 99],
{'credit_used': 788, 'discount': 262, 'due': 0, 'reason': None}),
('partial repair probe: credits after discount',
[{'cap': None,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 25},
{'credits': 250, 'fare': 3234, 'rider_rides': 0}, 99],
{'credit_used': 250, 'discount': 808, 'due': 2176, 'reason': None}),
('second regression',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': False,
'kind': 'pct',
'min_fare': 0,
'uses_left': 1,
'value': 25},
{'credits': 5000, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 1800, 'discount': 600, 'due': 0, 'reason': None}),
('normal control 1',
[{'cap': 0,
'expires_day': 100,
'first_ride_only': False,
'kind': 'flat',
'min_fare': 0,
'uses_left': 3,
'value': 500},
{'credits': 0, 'fare': 1050, 'rider_rides': 0}, 101],
{'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'}),
('normal control 2',
[{'cap': None,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1050,
'uses_left': 0,
'value': 50},
{'credits': 0, 'fare': 1000, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 0, 'due': 1000, 'reason': 'exhausted'}),
('normal control 3',
[{'cap': 800,
'expires_day': 100,
'first_ride_only': True,
'kind': 'pct',
'min_fare': 1000,
'uses_left': 1,
'value': 10},
{'credits': 0, 'fare': 2400, 'rider_rides': 0}, 100],
{'credit_used': 0, 'discount': 240, 'due': 2160, 'reason': None}),
('normal control 4',
[{'cap': 300,
'expires_day': 100,
'first_ride_only': True,
'kind': 'flat',
'min_fare': 1000,
'uses_left': 1,
'value': 1000},
{'credits': 0, 'fare': 2400, 'rider_rides': 5}, 99],
{'credit_used': 0, 'discount': 0, 'due': 2400, 'reason': 'not_first'})]]
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: credits after discount | {'credit_used': 2160, 'discount': 240, 'due': 0, 'reason': None} | {'credit_used': 2160, 'discount': 240, 'due': 0, 'reason': None} | Passed |
| partial repair probe: credits after discount | {'credit_used': 1730, 'discount': 1729, 'due': 0, 'reason': None} | {'credit_used': 1730, 'discount': 1729, 'due': 0, 'reason': None} | Passed |
| second regression | {'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None} | {'credit_used': 0, 'discount': 1099, 'due': 0, 'reason': None} | Passed |
| normal control 1 | {'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'exhausted'} | {'credit_used': 1000, 'discount': 0, 'due': 0, 'reason': 'exhausted'} | Passed |
| normal control 2 | {'credit_used': 250, 'discount': 0, 'due': 450, 'reason': 'min_fare'} | {'credit_used': 250, 'discount': 0, 'due': 450, 'reason': 'min_fare'} | Passed |
| normal control 3 | {'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'expired'} | {'credit_used': 0, 'discount': 0, 'due': 700, 'reason': 'expired'} | Passed |
| normal control 4 | {'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'} | {'credit_used': 0, 'discount': 0, 'due': 1050, 'reason': 'expired'} | Passed |
SHA-256 / b2097dde6bf3fabdeccb68368a65f4c95631fc0d0959ad666fa6ad7bd97005c4
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 2026-09-29T14:50:41.847387+00:00.
Case digest / a54a3039821ffe6f33be975848a17dde7fd155ec36d4c015ac0465f5164e83d1