FAILURE MAP
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FA-59746 / Subscription proration billing / Open access

Metered usage split at a mid-period price change: segment max scope · case 01

Peak meters bill the whole-period peak in both segments.

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

ROOT CAUSE

The max aggregation looks at all events instead of the segment.

THE FAILURE

The max aggregation looks at all events instead of the segment.

Unsuccessful approach: The attempt ignores single-reading 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(q for t, q in ev)
        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': [[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': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('normal control', {'events': [[15, 86], [15, 88]], 'change': 15, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3520]), ('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]), ('normal control', {'events': [], 'change': 6, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [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': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('normal control', {'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': [[19, 13], [29, 81], [12, 31], [12, 74]], 'change': 12, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3240]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'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': [[21, 53], [25, 86], [26, 43], [1, 19], [13, 55]], 'change': 13, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [475, 3440]), ('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': [[30, 18], [0, 63], [28, 43]], 'change': 28, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1575, 1720]), ('partial-repair probe', {'events': [[18, 32], [15, 45], [18, 6]], 'change': 18, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [450, 1280]), ('normal control', {'events': [[28, 32], [5, 0], [19, 71], [20, 79]], 'change': 19, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 2730]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [[15, 59]], 'change': 15, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 885]), ('normal control', {'events': [[28, 51], [11, 74], [16, 43], [3, 28]], 'change': 16, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [1850, 2040])], [('regression', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('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': [[0, 49], [11, 65], [3, 41]], 'change': 3, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [1225, 975]), ('partial-repair probe', {'events': [[25, 47], [12, 99], [8, 68]], 'change': 12, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [680, 3960]), ('normal control', {'events': [[28, 90], [11, 81], [2, 82]], 'change': 16, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [810, 1350]), ('normal control', {'events': [[10, 22], [11, 92], [13, 32], [10, 16], [9, 90]], 'change': 10, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [900, 1280]), ('normal control', {'events': [[7, 73], [11, 66], [21, 17]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [425, 0]), ('normal control', {'events': [[11, 1], [27, 17], [27, 80], [23, 84], [14, 2]], 'change': 23, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [20, 1200])], [('regression', {'events': [[30, 18], [0, 63], [28, 43]], 'change': 28, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1575, 1720]), ('regression', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('partial-repair probe', {'events': [[14, 64], [7, 0]], 'change': 14, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [0, 960]), ('partial-repair probe', {'events': [[26, 53], [18, 24], [13, 63]], 'change': 26, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1575, 2120]), ('normal control', {'events': [[9, 7]], 'change': 9, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 280]), ('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': [], 'change': 8, 'old_unit': 25, 'new_unit': 15, '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 fixtureActualExpectedOutcome
regression 0[2300, 1380][725, 1380]Failed
regression 1[960, 1440][900, 1440]Failed
partial-repair probe 2[2150, 3440][475, 3440]Failed
partial-repair probe 3[1950, 3120][1950, 3080]Failed
normal control 4[0, 3520][0, 3520]Passed
normal control 5[780, 0][780, 0]Passed
normal control 6[0, 0][0, 0]Passed
normal control 7[0, 0][0, 0]Passed

SHA-256 / f60f000b170c2a287f66e99f31750a4f8569de720cbe1266e6b325ba1ca82573

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) if len(v) > 1 else 0
        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': [[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': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('normal control', {'events': [[15, 86], [15, 88]], 'change': 15, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3520]), ('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]), ('normal control', {'events': [], 'change': 6, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [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': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('normal control', {'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': [[19, 13], [29, 81], [12, 31], [12, 74]], 'change': 12, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [0, 3240]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 15, 'agg': 'max'}, [0, 0]), ('normal control', {'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': [[21, 53], [25, 86], [26, 43], [1, 19], [13, 55]], 'change': 13, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [475, 3440]), ('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': [[30, 18], [0, 63], [28, 43]], 'change': 28, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1575, 1720]), ('partial-repair probe', {'events': [[18, 32], [15, 45], [18, 6]], 'change': 18, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [450, 1280]), ('normal control', {'events': [[28, 32], [5, 0], [19, 71], [20, 79]], 'change': 19, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 2730]), ('normal control', {'events': [], 'change': 1, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 0]), ('normal control', {'events': [[15, 59]], 'change': 15, 'old_unit': 25, 'new_unit': 15, 'agg': 'sum'}, [0, 885]), ('normal control', {'events': [[28, 51], [11, 74], [16, 43], [3, 28]], 'change': 16, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [1850, 2040])], [('regression', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('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': [[0, 49], [11, 65], [3, 41]], 'change': 3, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [1225, 975]), ('partial-repair probe', {'events': [[25, 47], [12, 99], [8, 68]], 'change': 12, 'old_unit': 10, 'new_unit': 40, 'agg': 'max'}, [680, 3960]), ('normal control', {'events': [[28, 90], [11, 81], [2, 82]], 'change': 16, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [810, 1350]), ('normal control', {'events': [[10, 22], [11, 92], [13, 32], [10, 16], [9, 90]], 'change': 10, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [900, 1280]), ('normal control', {'events': [[7, 73], [11, 66], [21, 17]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'last'}, [425, 0]), ('normal control', {'events': [[11, 1], [27, 17], [27, 80], [23, 84], [14, 2]], 'change': 23, 'old_unit': 10, 'new_unit': 15, 'agg': 'last'}, [20, 1200])], [('regression', {'events': [[30, 18], [0, 63], [28, 43]], 'change': 28, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1575, 1720]), ('regression', {'events': [[19, 78], [29, 77], [0, 53]], 'change': 29, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1950, 3080]), ('partial-repair probe', {'events': [[14, 64], [7, 0]], 'change': 14, 'old_unit': 25, 'new_unit': 15, 'agg': 'max'}, [0, 960]), ('partial-repair probe', {'events': [[26, 53], [18, 24], [13, 63]], 'change': 26, 'old_unit': 25, 'new_unit': 40, 'agg': 'max'}, [1575, 2120]), ('normal control', {'events': [[9, 7]], 'change': 9, 'old_unit': 10, 'new_unit': 40, 'agg': 'last'}, [0, 280]), ('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': [], 'change': 8, 'old_unit': 25, 'new_unit': 15, '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 fixtureActualExpectedOutcome
regression 0[0, 1380][725, 1380]Failed
regression 1[900, 0][900, 1440]Failed
partial-repair probe 2[0, 3440][475, 3440]Failed
partial-repair probe 3[1950, 0][1950, 3080]Failed
normal control 4[0, 3520][0, 3520]Passed
normal control 5[780, 0][780, 0]Passed
normal control 6[0, 0][0, 0]Passed
normal control 7[0, 0][0, 0]Passed

SHA-256 / c3d83a71ceda35fa028994858c894bd73be29490f613f5eef4f37646f93457c8

HELD IN THE MEMBER ARCHIVE

The verified repair and its recorded checks are member-only.

This mechanism has 8 recorded checks per implementation. The open-access tier publishes the failure and the unsuccessful fix; the repaired source that passes every check, and the observations that prove it, are available to members.

Every case sharing this mechanism uses the same contract and the same repair, so this one record is held back for all of them.

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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 2026-09-29T14:46:39.128505+00:00.

Case digest / 5f4fe7bfcab91fe4b5b5f5ce18f7bfbcfa10b406e96b0c97a2cbdc094ef3d863