FA-59751 / Subscription proration billing / Open access
Metered usage split at a mid-period price change: segment pricing · case 01
Usage before an upgrade is billed at the new price.
ROOT CAUSE
Both segments are priced at the new unit price.
VERIFIED REPAIR
Restore the contract rule at the segment pricing step: use `return [agg(before) * x['old_unit'], agg(after) * x['new_unit']]`.
Unsuccessful approach: The attempt swaps the prices between segments.
Case contract
Input {events: [[day, qty]], change day, old_unit, new_unit, agg sum|max|last}. Events are ordered by day (stable). Events before the change day are aggregated and priced at old_unit; events on or after it at new_unit. Empty segments aggregate to 0; last means the chronologically last quantity. Return [old_charge, new_charge].
Why this case matters
Metered usage must be attributed to the price in effect when it occurred, with the aggregation mode applied per segment.
1 / The failure
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
ev = sorted(x['events'], key=lambda e: e[0])
before = [q for t, q in ev if t < x['change']]
after = [q for t, q in ev if t >= x['change']]
def agg(v):
if not v:
return 0
if x['agg'] == 'sum':
return sum(v)
if x['agg'] == 'max':
return max(v)
return v[-1]
return [agg(before) * x['new_unit'], agg(after) * x['new_unit']]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'events': [[7, 80], [27, 92], [3, 28], [21, 43], [0, 29]], 'change': 3, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [725, 1380]), ('regression', {'events': [[27, 13], [19, 6], [5, 44], [27, 90], [30, 96]], 'change': 30, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1440]), ('partial-repair probe', {'events': [[15, 86], [15, 88]], 'change': 15, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3520]), ('partial-repair probe', {'events': [[14, 21], [12, 35], [4, 90], [22, 95], [25, 43], [7, 60]], 'change': 12, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1425]), ('normal control', {'events': [], 'change': 27, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 6, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 0])], [('regression', {'events': [[27, 13], [19, 6], [5, 44], [27, 90], [30, 96]], 'change': 30, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1440]), ('regression', {'events': [[14, 21], [12, 35], [4, 90], [22, 95], [25, 43], [7, 60]], 'change': 12, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1425]), ('partial-repair probe', {'events': [[21, 53], [25, 86], [26, 43], [1, 19], [13, 55]], 'change': 13, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [475, 3440]), ('partial-repair probe', {'events': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('normal control', {'events': [], 'change': 13, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 8, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 26, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [], 'change': 5, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [0, 0])], [('regression', {'events': [[14, 21], [12, 35], [4, 90], [22, 95], [25, 43], [7, 60]], 'change': 12, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1425]), ('regression', {'events': [[21, 53], [25, 86], [26, 43], [1, 19], [13, 55]], 'change': 13, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [475, 3440]), ('partial-repair probe', {'events': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('partial-repair probe', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('normal control', {'events': [], 'change': 27, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 16, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 20, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [], 'change': 17, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [0, 0])], [('regression', {'events': [[21, 53], [25, 86], [26, 43], [1, 19], [13, 55]], 'change': 13, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [475, 3440]), ('regression', {'events': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('partial-repair probe', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('partial-repair probe', {'events': [[21, 57], [28, 66], [4, 55], [23, 20], [14, 65], [14, 74]], 'change': 4, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 2640]), ('normal control', {'events': [], 'change': 19, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [], 'change': 16, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 21, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 9, 'old_unit': 25, 'new_unit': 40, 'agg': 'sum'}, [0, 0])], [('regression', {'events': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('regression', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('partial-repair probe', {'events': [[30, 18], [0, 63], [28, 43]], 'change': 28, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1575, 1720]), ('partial-repair probe', {'events': [[19, 13], [29, 81], [12, 31], [12, 74]], 'change': 12, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3240]), ('normal control', {'events': [], 'change': 20, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 2, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [], 'change': 23, 'old_unit': 25, 'new_unit': 40, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 16, 'old_unit': 25, 'new_unit': 40, 'agg': 'sum'}, [0, 0])]]
for i, (label, args, expected) in enumerate(fixtures[N-1]):
check("%s %d" % (label, 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 |
|---|---|---|---|
| regression 0 | [435, 1380] | [725, 1380] | Failed |
| regression 1 | [1350, 1440] | [900, 1440] | Failed |
| partial-repair probe 2 | [0, 3520] | [0, 3520] | Passed |
| partial-repair probe 3 | [1350, 1425] | [900, 1425] | Failed |
| normal control 4 | [0, 0] | [0, 0] | Passed |
| normal control 5 | [0, 0] | [0, 0] | Passed |
| normal control 6 | [0, 0] | [0, 0] | Passed |
| normal control 7 | [0, 0] | [0, 0] | Passed |
SHA-256 / fde637c5108b893039a3c663894b087bbaafa86ad05148f67b5bd5cf275fa178
2 / The unsuccessful fix
Exit 1"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
ev = sorted(x['events'], key=lambda e: e[0])
before = [q for t, q in ev if t < x['change']]
after = [q for t, q in ev if t >= x['change']]
def agg(v):
if not v:
return 0
if x['agg'] == 'sum':
return sum(v)
if x['agg'] == 'max':
return max(v)
return v[-1]
return [agg(before) * x['new_unit'], agg(after) * x['old_unit']]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'events': [[7, 80], [27, 92], [3, 28], [21, 43], [0, 29]], 'change': 3, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [725, 1380]), ('regression', {'events': [[27, 13], [19, 6], [5, 44], [27, 90], [30, 96]], 'change': 30, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1440]), ('partial-repair probe', {'events': [[15, 86], [15, 88]], 'change': 15, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3520]), ('partial-repair probe', {'events': [[14, 21], [12, 35], [4, 90], [22, 95], [25, 43], [7, 60]], 'change': 12, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1425]), ('normal control', {'events': [], 'change': 27, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 6, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 0])], [('regression', {'events': [[27, 13], [19, 6], [5, 44], [27, 90], [30, 96]], 'change': 30, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1440]), ('regression', {'events': [[14, 21], [12, 35], [4, 90], [22, 95], [25, 43], [7, 60]], 'change': 12, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1425]), ('partial-repair probe', {'events': [[21, 53], [25, 86], [26, 43], [1, 19], [13, 55]], 'change': 13, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [475, 3440]), ('partial-repair probe', {'events': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('normal control', {'events': [], 'change': 13, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 8, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 26, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [], 'change': 5, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [0, 0])], [('regression', {'events': [[14, 21], [12, 35], [4, 90], [22, 95], [25, 43], [7, 60]], 'change': 12, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1425]), ('regression', {'events': [[21, 53], [25, 86], [26, 43], [1, 19], [13, 55]], 'change': 13, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [475, 3440]), ('partial-repair probe', {'events': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('partial-repair probe', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('normal control', {'events': [], 'change': 27, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 16, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 20, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [], 'change': 17, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [0, 0])], [('regression', {'events': [[21, 53], [25, 86], [26, 43], [1, 19], [13, 55]], 'change': 13, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [475, 3440]), ('regression', {'events': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('partial-repair probe', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('partial-repair probe', {'events': [[21, 57], [28, 66], [4, 55], [23, 20], [14, 65], [14, 74]], 'change': 4, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 2640]), ('normal control', {'events': [], 'change': 19, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [], 'change': 16, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 21, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 9, 'old_unit': 25, 'new_unit': 40, 'agg': 'sum'}, [0, 0])], [('regression', {'events': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('regression', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('partial-repair probe', {'events': [[30, 18], [0, 63], [28, 43]], 'change': 28, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1575, 1720]), ('partial-repair probe', {'events': [[19, 13], [29, 81], [12, 31], [12, 74]], 'change': 12, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3240]), ('normal control', {'events': [], 'change': 20, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 2, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [], 'change': 23, 'old_unit': 25, 'new_unit': 40, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 16, 'old_unit': 25, 'new_unit': 40, 'agg': 'sum'}, [0, 0])]]
for i, (label, args, expected) in enumerate(fixtures[N-1]):
check("%s %d" % (label, 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 |
|---|---|---|---|
| regression 0 | [435, 2300] | [725, 1380] | Failed |
| regression 1 | [1350, 960] | [900, 1440] | Failed |
| partial-repair probe 2 | [0, 2200] | [0, 3520] | Failed |
| partial-repair probe 3 | [1350, 950] | [900, 1425] | Failed |
| normal control 4 | [0, 0] | [0, 0] | Passed |
| normal control 5 | [0, 0] | [0, 0] | Passed |
| normal control 6 | [0, 0] | [0, 0] | Passed |
| normal control 7 | [0, 0] | [0, 0] | Passed |
SHA-256 / c133bc2eb7fb9ab1449c3e4f546c1f3bd3544f88f50dd0409aecff002e72e29b
3 / The verified repair
Exit 0"""Failure Map reference implementation. Python standard library only."""
import json
N = 1
observations = []
def solve(x):
ev = sorted(x['events'], key=lambda e: e[0])
before = [q for t, q in ev if t < x['change']]
after = [q for t, q in ev if t >= x['change']]
def agg(v):
if not v:
return 0
if x['agg'] == 'sum':
return sum(v)
if x['agg'] == 'max':
return max(v)
return v[-1]
return [agg(before) * x['old_unit'], agg(after) * x['new_unit']]
def check(label, actual, expected):
observations.append({"check": label, "actual": actual, "expected": expected, "passed": actual == expected})
fixtures = [[('regression', {'events': [[7, 80], [27, 92], [3, 28], [21, 43], [0, 29]], 'change': 3, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [725, 1380]), ('regression', {'events': [[27, 13], [19, 6], [5, 44], [27, 90], [30, 96]], 'change': 30, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1440]), ('partial-repair probe', {'events': [[15, 86], [15, 88]], 'change': 15, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3520]), ('partial-repair probe', {'events': [[14, 21], [12, 35], [4, 90], [22, 95], [25, 43], [7, 60]], 'change': 12, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1425]), ('normal control', {'events': [], 'change': 27, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 6, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 0])], [('regression', {'events': [[27, 13], [19, 6], [5, 44], [27, 90], [30, 96]], 'change': 30, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1440]), ('regression', {'events': [[14, 21], [12, 35], [4, 90], [22, 95], [25, 43], [7, 60]], 'change': 12, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1425]), ('partial-repair probe', {'events': [[21, 53], [25, 86], [26, 43], [1, 19], [13, 55]], 'change': 13, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [475, 3440]), ('partial-repair probe', {'events': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('normal control', {'events': [], 'change': 13, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 8, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 26, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [], 'change': 5, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [0, 0])], [('regression', {'events': [[14, 21], [12, 35], [4, 90], [22, 95], [25, 43], [7, 60]], 'change': 12, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [900, 1425]), ('regression', {'events': [[21, 53], [25, 86], [26, 43], [1, 19], [13, 55]], 'change': 13, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [475, 3440]), ('partial-repair probe', {'events': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('partial-repair probe', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('normal control', {'events': [], 'change': 27, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 16, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 20, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [], 'change': 17, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [0, 0])], [('regression', {'events': [[21, 53], [25, 86], [26, 43], [1, 19], [13, 55]], 'change': 13, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [475, 3440]), ('regression', {'events': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('partial-repair probe', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('partial-repair probe', {'events': [[21, 57], [28, 66], [4, 55], [23, 20], [14, 65], [14, 74]], 'change': 4, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 2640]), ('normal control', {'events': [], 'change': 19, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [], 'change': 16, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [], 'change': 21, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 9, 'old_unit': 25, 'new_unit': 40, 'agg': 'sum'}, [0, 0])], [('regression', {'events': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('regression', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('partial-repair probe', {'events': [[30, 18], [0, 63], [28, 43]], 'change': 28, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1575, 1720]), ('partial-repair probe', {'events': [[19, 13], [29, 81], [12, 31], [12, 74]], 'change': 12, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3240]), ('normal control', {'events': [], 'change': 20, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 2, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [], 'change': 23, 'old_unit': 25, 'new_unit': 40, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [], 'change': 16, 'old_unit': 25, 'new_unit': 40, 'agg': 'sum'}, [0, 0])]]
for i, (label, args, expected) in enumerate(fixtures[N-1]):
check("%s %d" % (label, 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 |
|---|---|---|---|
| regression 0 | [725, 1380] | [725, 1380] | Passed |
| regression 1 | [900, 1440] | [900, 1440] | Passed |
| partial-repair probe 2 | [0, 3520] | [0, 3520] | Passed |
| partial-repair probe 3 | [900, 1425] | [900, 1425] | Passed |
| normal control 4 | [0, 0] | [0, 0] | Passed |
| normal control 5 | [0, 0] | [0, 0] | Passed |
| normal control 6 | [0, 0] | [0, 0] | Passed |
| normal control 7 | [0, 0] | [0, 0] | Passed |
SHA-256 / bdbd44b9e3d27460d5f823259e5940bd8ed3576d7f18ea09bdc95894acb85261
Verification & scope
A deterministic teaching model of a stipulated billing rule. It makes no claim to reproduce any billing provider's exact behaviour and is not billing software. 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 2026-09-29T14:46:39.166973+00:00.
Case digest / d6be5cea4a7da5035bd48f2ba39eef11155ce4582e8fa7ca742ed0308ed0cbcb