FA-59736 / Subscription proration billing / Open access

Metered usage split at a mid-period price change: change day ownership · case 01

Usage recorded on the change day is billed at both prices.

Verified by executionVariant 1 · 8 checks per implementationDownload source bundle ↓JSON ↗

ROOT CAUSE

The old-price segment includes the change day.

VERIFIED REPAIR

Restore the contract rule at the change day ownership step: use `before = [q for t, q in ev if t < x['change']]`.

Unsuccessful approach: The attempt drops the day before the change from the old segment entirely.

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['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': [[0, 58], [18, 62], [19, 45], [13, 16], [4, 63]], 'change': 19, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [1550, 675]), ('regression', {'events': [[15, 86], [15, 88]], 'change': 15, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3520]), ('partial-repair probe', {'events': [[10, 22], [11, 92], [13, 32], [10, 16], [9, 90]], 'change': 10, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [900, 1280]), ('partial-repair probe', {'events': [[26, 90], [27, 37], [29, 38]], 'change': 27, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [900, 1125]), ('normal control', {'events': [[7, 80], [27, 92], [3, 28], [21, 43], [0, 29]], 'change': 3, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [725, 1380]), ('normal control', {'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': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('normal control', {'events': [], 'change': 27, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [0, 0])], [('regression', {'events': [[10, 22], [11, 92], [13, 32], [10, 16], [9, 90]], 'change': 10, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [900, 1280]), ('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': [[26, 90], [27, 37], [29, 38]], 'change': 27, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [900, 1125]), ('partial-repair probe', {'events': [[0, 23], [4, 89], [5, 3]], 'change': 5, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [2225, 45]), ('normal control', {'events': [], 'change': 6, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('normal control', {'events': [[30, 18], [0, 63], [28, 43]], 'change': 28, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1575, 1720]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0])], [('regression', {'events': [[26, 90], [27, 37], [29, 38]], 'change': 27, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [900, 1125]), ('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': [[21, 60], [30, 96], [16, 57], [5, 93], [30, 96]], 'change': 22, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [600, 3840]), ('partial-repair probe', {'events': [[9, 69], [8, 24], [20, 22], [10, 85]], 'change': 9, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [600, 3400]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [[28, 90], [11, 81], [2, 82]], 'change': 16, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [810, 1350]), ('normal control', {'events': [[7, 73], [11, 66], [21, 17]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [425, 0]), ('normal control', {'events': [[18, 32], [15, 45], [18, 6]], 'change': 18, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [450, 1280])], [('regression', {'events': [[0, 23], [4, 89], [5, 3]], 'change': 5, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [2225, 45]), ('regression', {'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]), ('partial-repair probe', {'events': [[25, 50], [26, 27], [26, 6], [28, 92], [21, 23], [5, 66]], 'change': 26, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [500, 3680]), ('partial-repair probe', {'events': [[2, 41], [3, 45], [30, 93]], 'change': 3, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [410, 2070]), ('normal control', {'events': [[14, 36]], 'change': 9, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [0, 1440]), ('normal control', {'events': [], 'change': 13, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [[0, 49], [11, 65], [3, 41]], 'change': 3, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [1225, 975]), ('normal control', {'events': [[22, 54], [5, 92], [13, 67], [15, 87], [19, 0]], 'change': 19, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [920, 2160])], [('regression', {'events': [[9, 69], [8, 24], [20, 22], [10, 85]], 'change': 9, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [600, 3400]), ('regression', {'events': [[19, 13], [29, 81], [12, 31], [12, 74]], 'change': 12, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3240]), ('partial-repair probe', {'events': [[13, 87], [30, 41], [0, 12], [5, 55], [6, 92], [1, 78]], 'change': 1, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [120, 1380]), ('partial-repair probe', {'events': [[3, 68], [1, 51], [0, 25], [2, 68]], 'change': 3, 'old_unit': 25, 'new_unit': 40, 'agg': 'sum'}, [3600, 2720]), ('normal control', {'events': [], 'change': 8, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [[10, 87], [0, 71], [24, 27], [26, 7]], 'change': 21, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [2175, 1080]), ('normal control', {'events': [[20, 35]], 'change': 24, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [875, 0]), ('normal control', {'events': [], 'change': 26, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [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	`[1125, 675]`	`[1550, 675]`	Failed
regression 1	`[2200, 3520]`	`[0, 3520]`	Failed
partial-repair probe 2	`[160, 1280]`	`[900, 1280]`	Failed
partial-repair probe 3	`[1270, 1125]`	`[900, 1125]`	Failed
normal control 4	`[725, 1380]`	`[725, 1380]`	Passed
normal control 5	`[900, 1425]`	`[900, 1425]`	Passed
normal control 6	`[780, 0]`	`[780, 0]`	Passed
normal control 7	`[0, 0]`	`[0, 0]`	Passed

SHA-256 / dfd2301df1f1ba8f5260e823c8bcdcf760206a5cb6f4d539cfff8bf32345827b

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'] - 1]
    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': [[0, 58], [18, 62], [19, 45], [13, 16], [4, 63]], 'change': 19, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [1550, 675]), ('regression', {'events': [[15, 86], [15, 88]], 'change': 15, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3520]), ('partial-repair probe', {'events': [[10, 22], [11, 92], [13, 32], [10, 16], [9, 90]], 'change': 10, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [900, 1280]), ('partial-repair probe', {'events': [[26, 90], [27, 37], [29, 38]], 'change': 27, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [900, 1125]), ('normal control', {'events': [[7, 80], [27, 92], [3, 28], [21, 43], [0, 29]], 'change': 3, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [725, 1380]), ('normal control', {'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': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('normal control', {'events': [], 'change': 27, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [0, 0])], [('regression', {'events': [[10, 22], [11, 92], [13, 32], [10, 16], [9, 90]], 'change': 10, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [900, 1280]), ('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': [[26, 90], [27, 37], [29, 38]], 'change': 27, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [900, 1125]), ('partial-repair probe', {'events': [[0, 23], [4, 89], [5, 3]], 'change': 5, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [2225, 45]), ('normal control', {'events': [], 'change': 6, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('normal control', {'events': [[30, 18], [0, 63], [28, 43]], 'change': 28, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1575, 1720]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0])], [('regression', {'events': [[26, 90], [27, 37], [29, 38]], 'change': 27, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [900, 1125]), ('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': [[21, 60], [30, 96], [16, 57], [5, 93], [30, 96]], 'change': 22, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [600, 3840]), ('partial-repair probe', {'events': [[9, 69], [8, 24], [20, 22], [10, 85]], 'change': 9, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [600, 3400]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [[28, 90], [11, 81], [2, 82]], 'change': 16, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [810, 1350]), ('normal control', {'events': [[7, 73], [11, 66], [21, 17]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [425, 0]), ('normal control', {'events': [[18, 32], [15, 45], [18, 6]], 'change': 18, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [450, 1280])], [('regression', {'events': [[0, 23], [4, 89], [5, 3]], 'change': 5, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [2225, 45]), ('regression', {'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]), ('partial-repair probe', {'events': [[25, 50], [26, 27], [26, 6], [28, 92], [21, 23], [5, 66]], 'change': 26, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [500, 3680]), ('partial-repair probe', {'events': [[2, 41], [3, 45], [30, 93]], 'change': 3, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [410, 2070]), ('normal control', {'events': [[14, 36]], 'change': 9, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [0, 1440]), ('normal control', {'events': [], 'change': 13, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [[0, 49], [11, 65], [3, 41]], 'change': 3, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [1225, 975]), ('normal control', {'events': [[22, 54], [5, 92], [13, 67], [15, 87], [19, 0]], 'change': 19, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [920, 2160])], [('regression', {'events': [[9, 69], [8, 24], [20, 22], [10, 85]], 'change': 9, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [600, 3400]), ('regression', {'events': [[19, 13], [29, 81], [12, 31], [12, 74]], 'change': 12, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3240]), ('partial-repair probe', {'events': [[13, 87], [30, 41], [0, 12], [5, 55], [6, 92], [1, 78]], 'change': 1, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [120, 1380]), ('partial-repair probe', {'events': [[3, 68], [1, 51], [0, 25], [2, 68]], 'change': 3, 'old_unit': 25, 'new_unit': 40, 'agg': 'sum'}, [3600, 2720]), ('normal control', {'events': [], 'change': 8, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [[10, 87], [0, 71], [24, 27], [26, 7]], 'change': 21, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [2175, 1080]), ('normal control', {'events': [[20, 35]], 'change': 24, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [875, 0]), ('normal control', {'events': [], 'change': 26, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [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	`[400, 675]`	`[1550, 675]`	Failed
regression 1	`[0, 3520]`	`[0, 3520]`	Passed
partial-repair probe 2	`[0, 1280]`	`[900, 1280]`	Failed
partial-repair probe 3	`[0, 1125]`	`[900, 1125]`	Failed
normal control 4	`[725, 1380]`	`[725, 1380]`	Passed
normal control 5	`[900, 1425]`	`[900, 1425]`	Passed
normal control 6	`[780, 0]`	`[780, 0]`	Passed
normal control 7	`[0, 0]`	`[0, 0]`	Passed

SHA-256 / a4d53d88c6d076f57cbdb3c152475b228616fda8a5a6ed6b09a29058d4094c34

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': [[0, 58], [18, 62], [19, 45], [13, 16], [4, 63]], 'change': 19, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [1550, 675]), ('regression', {'events': [[15, 86], [15, 88]], 'change': 15, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3520]), ('partial-repair probe', {'events': [[10, 22], [11, 92], [13, 32], [10, 16], [9, 90]], 'change': 10, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [900, 1280]), ('partial-repair probe', {'events': [[26, 90], [27, 37], [29, 38]], 'change': 27, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [900, 1125]), ('normal control', {'events': [[7, 80], [27, 92], [3, 28], [21, 43], [0, 29]], 'change': 3, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [725, 1380]), ('normal control', {'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': [[10, 54], [8, 78]], 'change': 23, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [780, 0]), ('normal control', {'events': [], 'change': 27, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [0, 0])], [('regression', {'events': [[10, 22], [11, 92], [13, 32], [10, 16], [9, 90]], 'change': 10, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [900, 1280]), ('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': [[26, 90], [27, 37], [29, 38]], 'change': 27, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [900, 1125]), ('partial-repair probe', {'events': [[0, 23], [4, 89], [5, 3]], 'change': 5, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [2225, 45]), ('normal control', {'events': [], 'change': 6, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('normal control', {'events': [[30, 18], [0, 63], [28, 43]], 'change': 28, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1575, 1720]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0])], [('regression', {'events': [[26, 90], [27, 37], [29, 38]], 'change': 27, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [900, 1125]), ('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': [[21, 60], [30, 96], [16, 57], [5, 93], [30, 96]], 'change': 22, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [600, 3840]), ('partial-repair probe', {'events': [[9, 69], [8, 24], [20, 22], [10, 85]], 'change': 9, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [600, 3400]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [[28, 90], [11, 81], [2, 82]], 'change': 16, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [810, 1350]), ('normal control', {'events': [[7, 73], [11, 66], [21, 17]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [425, 0]), ('normal control', {'events': [[18, 32], [15, 45], [18, 6]], 'change': 18, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [450, 1280])], [('regression', {'events': [[0, 23], [4, 89], [5, 3]], 'change': 5, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [2225, 45]), ('regression', {'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]), ('partial-repair probe', {'events': [[25, 50], [26, 27], [26, 6], [28, 92], [21, 23], [5, 66]], 'change': 26, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [500, 3680]), ('partial-repair probe', {'events': [[2, 41], [3, 45], [30, 93]], 'change': 3, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [410, 2070]), ('normal control', {'events': [[14, 36]], 'change': 9, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [0, 1440]), ('normal control', {'events': [], 'change': 13, 'old_unit': 10, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [[0, 49], [11, 65], [3, 41]], 'change': 3, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [1225, 975]), ('normal control', {'events': [[22, 54], [5, 92], [13, 67], [15, 87], [19, 0]], 'change': 19, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [920, 2160])], [('regression', {'events': [[9, 69], [8, 24], [20, 22], [10, 85]], 'change': 9, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [600, 3400]), ('regression', {'events': [[19, 13], [29, 81], [12, 31], [12, 74]], 'change': 12, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3240]), ('partial-repair probe', {'events': [[13, 87], [30, 41], [0, 12], [5, 55], [6, 92], [1, 78]], 'change': 1, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [120, 1380]), ('partial-repair probe', {'events': [[3, 68], [1, 51], [0, 25], [2, 68]], 'change': 3, 'old_unit': 25, 'new_unit': 40, 'agg': 'sum'}, [3600, 2720]), ('normal control', {'events': [], 'change': 8, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 0]), ('normal control', {'events': [[10, 87], [0, 71], [24, 27], [26, 7]], 'change': 21, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [2175, 1080]), ('normal control', {'events': [[20, 35]], 'change': 24, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [875, 0]), ('normal control', {'events': [], 'change': 26, 'old_unit': 25, 'new_unit': 15, 'agg': 'last'}, [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	`[1550, 675]`	`[1550, 675]`	Passed
regression 1	`[0, 3520]`	`[0, 3520]`	Passed
partial-repair probe 2	`[900, 1280]`	`[900, 1280]`	Passed
partial-repair probe 3	`[900, 1125]`	`[900, 1125]`	Passed
normal control 4	`[725, 1380]`	`[725, 1380]`	Passed
normal control 5	`[900, 1425]`	`[900, 1425]`	Passed
normal control 6	`[780, 0]`	`[780, 0]`	Passed
normal control 7	`[0, 0]`	`[0, 0]`	Passed

SHA-256 / 1b6beb2ebfdfe639a041f7b0ef895f81697cead6183070655d24fea1294e4561

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 2026-09-29T14:46:39.125447+00:00.

Case digest / e5973b885b5d70d05783ca89723ec905994aa0e5551733d95de3179034715439