FA-83541 / Card game rule engines / Open access
Sub-runs inside a longer run are also scored · case 01
A four-card run also scores its two three-card sub-runs.
ROOT CAUSE
The run loop does not stop after the longest run length.
VERIFIED REPAIR
Score only the longest run length found.
Unsuccessful approach: Scoring the length once ignores double and triple runs.
Case contract
Input [four_card_hand, starter, is_crib]. Fifteens (any 2-5 cards, A=1, T/J/Q/K=10) score 2; each pair 2; runs score their length for every distinct combination of the longest run length; a four-card hand flush scores 4 (+1 with the starter) but the crib scores only a five-card flush; a hand jack matching the starter suit scores 1. Return the total.
Why this case matters
Card-game engines, scoring apps and online tables apply this rule automatically on every hand.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import itertools
N = 1
observations = []
def solve(x):
hand, starter, crib = x
order = 'A23456789TJQK'
cards = hand + [starter]
def val(c):
return min(order.index(c[0]) + 1, 10)
score = 0
for k in range(2, 6):
for combo in itertools.combinations(cards, k):
if sum(val(c) for c in combo) == 15:
score += 2
for a, b in itertools.combinations(cards, 2):
if a[0] == b[0]:
score += 2
ranks = sorted(order.index(c[0]) for c in cards)
for size in (5, 4, 3):
runs = 0
for combo in itertools.combinations(ranks, size):
if all(combo[i + 1] == combo[i] + 1 for i in range(size - 1)):
runs += 1
if runs:
score += runs * size
suits = [c[1] for c in hand]
if len(set(suits)) == 1:
if starter[1] == suits[0]:
score += 5
elif not crib:
score += 4
for c in hand:
if c[0] == 'J' and c[1] == starter[1]:
score += 1
return score
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[[['AC', '9C', '4C', 'TC'], '5S', True], 6], [[['KD', 'AH', '4S', '8D'], 'QH', False], 4], [[['3C', '3S', '4S', '5H'], '2D', False], 12], [[['5H', '9S', '6S', '2S'], 'AH', False], 4], [[['3S', '3H', '5H', 'JH'], 'QS', False], 6], [[['4C', 'QC', 'AC', '6C'], '5D', True], 9], [[['AD', 'AH', '2S', '3H'], 'KC', False], 12], [[['6H', '6D', '7S', '8H'], 'JD', False], 10]], [[[['6S', '8D', 'JD', '9C'], '6D', False], 7], [[['4S', '5D', '9D', 'TC'], 'QH', True], 4], [[['5H', 'KC', '2H', '3S'], '6H', True], 4], [[['9H', '7H', 'QH', 'TH'], 'JH', False], 9], [[['2C', '2S', '3S', '4H'], 'KD', False], 12], [[['4S', '3S', '9S', 'TD'], '7C', False], 0], [[['4C', '4S', '5S', '6H'], '3D', True], 14], [[['KH', '9S', 'JS', 'JD'], 'QC', False], 8]], [[[['3H', 'QS', '3C', '6S'], '8D', False], 2], [[['JH', '9H', '9S', 'TD'], '3H', False], 9], [[['6C', '2C', '7C', 'AC'], '4H', True], 2], [[['4D', 'AS', '7H', 'QD'], '9S', False], 2], [[['6S', '5S', 'QS', '8C'], 'AC', False], 4], [[['8S', 'QS', 'TH', '9C'], 'QC', False], 5], [[['QS', 'KC', 'JC', '6H'], 'TH', False], 4], [[['AC', 'AH', '2S', '3H'], 'AD', False], 15]], [[[['AD', 'AH', '2S', '3H'], 'KC', False], 12], [[['AS', '9S', 'TS', '2S'], '4C', False], 8], [[['TD', 'KS', 'TH', 'TS'], 'JC', False], 6], [[['KS', '5D', '3H', '8D'], 'KD', True], 6], [[['2S', '8S', 'TC', 'JS'], 'QH', False], 3], [[['9H', '7S', 'QH', '5H'], '3D', False], 4], [[['KH', '3C', '5H', '5C'], '8S', False], 6], [[['3H', '4D', '6D', 'AS'], '5S', False], 8]], [[[['8D', '8C', '9S', 'TH'], 'AH', True], 8], [[['2H', '5H', 'AC', '3S'], '8C', False], 5], [[['AD', '4S', '9D', '4H'], 'TH', False], 6], [[['9S', '9C', 'JH', 'KS'], '3H', False], 3], [[['KD', 'TC', 'QS', '4D'], 'TH', True], 2], [[['QD', '8S', 'JC', '5H'], '6C', False], 5], [[['6H', '6D', '7S', '8H'], 'JD', False], 10], [[['5S', 'TD', 'JC', 'QH'], 'KS', False], 12]]]
for i, (args, expected) in enumerate(fixtures[N-1]):
check("hand score case %d" % i, 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 |
|---|---|---|---|
| hand score case 0 | 6 | 6 | Passed |
| hand score case 1 | 4 | 4 | Passed |
| hand score case 2 | 24 | 12 | Failed |
| hand score case 3 | 4 | 4 | Passed |
| hand score case 4 | 6 | 6 | Passed |
| hand score case 5 | 9 | 9 | Passed |
| hand score case 6 | 12 | 12 | Passed |
| hand score case 7 | 10 | 10 | Passed |
SHA-256 / dbba51ea489467afe08aff7f8eac5d049c6c38a4e53b2065cde7ec36c245a8d1
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
import itertools
N = 1
observations = []
def solve(x):
hand, starter, crib = x
order = 'A23456789TJQK'
cards = hand + [starter]
def val(c):
return min(order.index(c[0]) + 1, 10)
score = 0
for k in range(2, 6):
for combo in itertools.combinations(cards, k):
if sum(val(c) for c in combo) == 15:
score += 2
for a, b in itertools.combinations(cards, 2):
if a[0] == b[0]:
score += 2
ranks = sorted(order.index(c[0]) for c in cards)
for size in (5, 4, 3):
runs = 0
for combo in itertools.combinations(ranks, size):
if all(combo[i + 1] == combo[i] + 1 for i in range(size - 1)):
runs += 1
if runs:
score += size
break
suits = [c[1] for c in hand]
if len(set(suits)) == 1:
if starter[1] == suits[0]:
score += 5
elif not crib:
score += 4
for c in hand:
if c[0] == 'J' and c[1] == starter[1]:
score += 1
return score
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[[['AC', '9C', '4C', 'TC'], '5S', True], 6], [[['KD', 'AH', '4S', '8D'], 'QH', False], 4], [[['3C', '3S', '4S', '5H'], '2D', False], 12], [[['5H', '9S', '6S', '2S'], 'AH', False], 4], [[['3S', '3H', '5H', 'JH'], 'QS', False], 6], [[['4C', 'QC', 'AC', '6C'], '5D', True], 9], [[['AD', 'AH', '2S', '3H'], 'KC', False], 12], [[['6H', '6D', '7S', '8H'], 'JD', False], 10]], [[[['6S', '8D', 'JD', '9C'], '6D', False], 7], [[['4S', '5D', '9D', 'TC'], 'QH', True], 4], [[['5H', 'KC', '2H', '3S'], '6H', True], 4], [[['9H', '7H', 'QH', 'TH'], 'JH', False], 9], [[['2C', '2S', '3S', '4H'], 'KD', False], 12], [[['4S', '3S', '9S', 'TD'], '7C', False], 0], [[['4C', '4S', '5S', '6H'], '3D', True], 14], [[['KH', '9S', 'JS', 'JD'], 'QC', False], 8]], [[[['3H', 'QS', '3C', '6S'], '8D', False], 2], [[['JH', '9H', '9S', 'TD'], '3H', False], 9], [[['6C', '2C', '7C', 'AC'], '4H', True], 2], [[['4D', 'AS', '7H', 'QD'], '9S', False], 2], [[['6S', '5S', 'QS', '8C'], 'AC', False], 4], [[['8S', 'QS', 'TH', '9C'], 'QC', False], 5], [[['QS', 'KC', 'JC', '6H'], 'TH', False], 4], [[['AC', 'AH', '2S', '3H'], 'AD', False], 15]], [[[['AD', 'AH', '2S', '3H'], 'KC', False], 12], [[['AS', '9S', 'TS', '2S'], '4C', False], 8], [[['TD', 'KS', 'TH', 'TS'], 'JC', False], 6], [[['KS', '5D', '3H', '8D'], 'KD', True], 6], [[['2S', '8S', 'TC', 'JS'], 'QH', False], 3], [[['9H', '7S', 'QH', '5H'], '3D', False], 4], [[['KH', '3C', '5H', '5C'], '8S', False], 6], [[['3H', '4D', '6D', 'AS'], '5S', False], 8]], [[[['8D', '8C', '9S', 'TH'], 'AH', True], 8], [[['2H', '5H', 'AC', '3S'], '8C', False], 5], [[['AD', '4S', '9D', '4H'], 'TH', False], 6], [[['9S', '9C', 'JH', 'KS'], '3H', False], 3], [[['KD', 'TC', 'QS', '4D'], 'TH', True], 2], [[['QD', '8S', 'JC', '5H'], '6C', False], 5], [[['6H', '6D', '7S', '8H'], 'JD', False], 10], [[['5S', 'TD', 'JC', 'QH'], 'KS', False], 12]]]
for i, (args, expected) in enumerate(fixtures[N-1]):
check("hand score case %d" % i, 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 |
|---|---|---|---|
| hand score case 0 | 6 | 6 | Passed |
| hand score case 1 | 4 | 4 | Passed |
| hand score case 2 | 8 | 12 | Failed |
| hand score case 3 | 4 | 4 | Passed |
| hand score case 4 | 6 | 6 | Passed |
| hand score case 5 | 9 | 9 | Passed |
| hand score case 6 | 9 | 12 | Failed |
| hand score case 7 | 7 | 10 | Failed |
SHA-256 / b1f94fa4940068afba72f4ebcf0e91aaa1595e4453d31bfd7460d59fc8662f74
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
import itertools
N = 1
observations = []
def solve(x):
hand, starter, crib = x
order = 'A23456789TJQK'
cards = hand + [starter]
def val(c):
return min(order.index(c[0]) + 1, 10)
score = 0
for k in range(2, 6):
for combo in itertools.combinations(cards, k):
if sum(val(c) for c in combo) == 15:
score += 2
for a, b in itertools.combinations(cards, 2):
if a[0] == b[0]:
score += 2
ranks = sorted(order.index(c[0]) for c in cards)
for size in (5, 4, 3):
runs = 0
for combo in itertools.combinations(ranks, size):
if all(combo[i + 1] == combo[i] + 1 for i in range(size - 1)):
runs += 1
if runs:
score += runs * size
break
suits = [c[1] for c in hand]
if len(set(suits)) == 1:
if starter[1] == suits[0]:
score += 5
elif not crib:
score += 4
for c in hand:
if c[0] == 'J' and c[1] == starter[1]:
score += 1
return score
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[[[['AC', '9C', '4C', 'TC'], '5S', True], 6], [[['KD', 'AH', '4S', '8D'], 'QH', False], 4], [[['3C', '3S', '4S', '5H'], '2D', False], 12], [[['5H', '9S', '6S', '2S'], 'AH', False], 4], [[['3S', '3H', '5H', 'JH'], 'QS', False], 6], [[['4C', 'QC', 'AC', '6C'], '5D', True], 9], [[['AD', 'AH', '2S', '3H'], 'KC', False], 12], [[['6H', '6D', '7S', '8H'], 'JD', False], 10]], [[[['6S', '8D', 'JD', '9C'], '6D', False], 7], [[['4S', '5D', '9D', 'TC'], 'QH', True], 4], [[['5H', 'KC', '2H', '3S'], '6H', True], 4], [[['9H', '7H', 'QH', 'TH'], 'JH', False], 9], [[['2C', '2S', '3S', '4H'], 'KD', False], 12], [[['4S', '3S', '9S', 'TD'], '7C', False], 0], [[['4C', '4S', '5S', '6H'], '3D', True], 14], [[['KH', '9S', 'JS', 'JD'], 'QC', False], 8]], [[[['3H', 'QS', '3C', '6S'], '8D', False], 2], [[['JH', '9H', '9S', 'TD'], '3H', False], 9], [[['6C', '2C', '7C', 'AC'], '4H', True], 2], [[['4D', 'AS', '7H', 'QD'], '9S', False], 2], [[['6S', '5S', 'QS', '8C'], 'AC', False], 4], [[['8S', 'QS', 'TH', '9C'], 'QC', False], 5], [[['QS', 'KC', 'JC', '6H'], 'TH', False], 4], [[['AC', 'AH', '2S', '3H'], 'AD', False], 15]], [[[['AD', 'AH', '2S', '3H'], 'KC', False], 12], [[['AS', '9S', 'TS', '2S'], '4C', False], 8], [[['TD', 'KS', 'TH', 'TS'], 'JC', False], 6], [[['KS', '5D', '3H', '8D'], 'KD', True], 6], [[['2S', '8S', 'TC', 'JS'], 'QH', False], 3], [[['9H', '7S', 'QH', '5H'], '3D', False], 4], [[['KH', '3C', '5H', '5C'], '8S', False], 6], [[['3H', '4D', '6D', 'AS'], '5S', False], 8]], [[[['8D', '8C', '9S', 'TH'], 'AH', True], 8], [[['2H', '5H', 'AC', '3S'], '8C', False], 5], [[['AD', '4S', '9D', '4H'], 'TH', False], 6], [[['9S', '9C', 'JH', 'KS'], '3H', False], 3], [[['KD', 'TC', 'QS', '4D'], 'TH', True], 2], [[['QD', '8S', 'JC', '5H'], '6C', False], 5], [[['6H', '6D', '7S', '8H'], 'JD', False], 10], [[['5S', 'TD', 'JC', 'QH'], 'KS', False], 12]]]
for i, (args, expected) in enumerate(fixtures[N-1]):
check("hand score case %d" % i, 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 |
|---|---|---|---|
| hand score case 0 | 6 | 6 | Passed |
| hand score case 1 | 4 | 4 | Passed |
| hand score case 2 | 12 | 12 | Passed |
| hand score case 3 | 4 | 4 | Passed |
| hand score case 4 | 6 | 6 | Passed |
| hand score case 5 | 9 | 9 | Passed |
| hand score case 6 | 12 | 12 | Passed |
| hand score case 7 | 10 | 10 | Passed |
SHA-256 / 1c6977447567fff117e9fadeb944b6c6903194b80f6858d88216000e1f79b069
Verification & scope
A bounded toy rule contract stated explicitly in the contract field; cards are two-character codes (rank, suit). Not a complete implementation of any published rulebook or casino table rules. 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 2026-09-29T14:50:22.593537+00:00.
Case digest / df3976e49cd628056d6d68e08c161606ef85727602c922d6e4e9a180129d241d